@article{mallasto2022entropy,
  title={Entropy-regularized 2-Wasserstein distance between Gaussian measures},
  author={Mallasto, Anton and Gerolin, Augusto and Minh, H{\`a} Quang},
  journal={Information Geometry},
  volume={5},
  number={1},
  pages={289--323},
  year={2022},
  publisher={Springer}
}

@article{liu2025domain,
  title={Domain-Incremental Semantic Segmentation for Traffic Scenes},
  author={Liu, Yazhou and Chen, Haoqi and Lasang, Pongsak and Wu, Zheng},
  journal={IEEE Transactions on Intelligent Transportation Systems},
  year={2025},
  publisher={IEEE}
}
@article{li2025clms,
  title={CLMS: Bridging domain gaps in medical imaging segmentation with source-free continual learning for robust knowledge transfer and adaptation},
  author={Li, Weilu and Zhang, Yun and Zhou, Hao and Yang, Wenhan and Xie, Zhi and He, Yao},
  journal={Medical Image Analysis},
  volume={100},
  pages={103404},
  year={2025},
  publisher={Elsevier}
}
@inproceedings{zhu2024low,
  title={Low-Rank Continual Pyramid Vision Transformer: Incrementally Segment Whole-Body Organs in CT with Light-Weighted Adaptation},
  author={Zhu, Vince and Ji, Zhanghexuan and Guo, Dazhou and Wang, Puyang and Xia, Yingda and Lu, Le and Ye, Xianghua and Zhu, Wei and Jin, Dakai},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={371--381},
  year={2024},
  organization={Springer}
}
@article{muralidhara2025domain,
  title={Domain-Incremental Semantic Segmentation for Autonomous Driving under Adverse Driving Conditions},
  author={Muralidhara, Shishir and Schuster, Ren{\'e} and Stricker, Didier},
  journal={arXiv preprint arXiv:2501.05246},
  year={2025}
}
@article{guo2024efficient,
  title={Efficient continual pre-training by mitigating the stability gap},
  author={Guo, Yiduo and Fu, Jie and Zhang, Huishuai and Zhao, Dongyan and Shen, Yikang},
  journal={arXiv preprint arXiv:2406.14833},
  year={2024}
}
@article{hess2023two,
  title={Two complementary perspectives to continual learning: Ask not only what to optimize, but also how},
  author={Hess, Timm and Tuytelaars, Tinne and van de Ven, Gido M},
  journal={arXiv preprint arXiv:2311.04898},
  year={2023}
}
@inproceedings{kamath2024expanding,
  title={The expanding scope of the stability gap: Unveiling its presence in joint incremental learning of homogeneous tasks},
  author={Kamath, Sandesh and Soutif-Cormerais, Albin and Van de Weijer, Joost and Raducanu, Bogdan},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={4182--4186},
  year={2024}
}

@InProceedings{C2,
  author = 	 "Jones, C.D. and Smith, A.B. and Roberts, E.F.",
  booktitle =        "Proceedings Title",
  organization = "IEEE",
  year = 	 "2003",
  volume = 	 "II",
  pages = 	 "803-806"
}
@article{garioud2022flair,
  title={Flair\# 1: semantic segmentation and domain adaptation dataset},
  author={Garioud, Anatol and Peillet, St{\'e}phane and Bookjans, Eva and Giordano, S{\'e}bastien and Wattrelos, Boris},
  journal={arXiv preprint arXiv:2211.12979},
  year={2022}
}
@article{wang2022s,
  title={S-prompts learning with pre-trained transformers: An occam’s razor for domain incremental learning},
  author={Wang, Yabin and Huang, Zhiwu and Hong, Xiaopeng},
  journal={Advances in Neural Information Processing Systems},
  volume={35},
  pages={5682--5695},
  year={2022}
}
@article{masana2022class,
  title={Class-incremental learning: survey and performance evaluation on image classification},
  author={Masana, Marc and Liu, Xialei and Twardowski, Bart{\l}omiej and Menta, Mikel and Bagdanov, Andrew D and Van De Weijer, Joost},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  volume={45},
  number={5},
  pages={5513--5533},
  year={2022},
  publisher={IEEE}
}
@article{huang2024domain,
  title={Domain Incremental Learning for Remote Sensing Semantic Segmentation with Multi-Feature Constraints in Graph Space},
  author={Huang, Wubiao and Ding, Mingtao and Deng, Fei},
  journal={IEEE Transactions on Geoscience and Remote Sensing},
  year={2024},
  publisher={IEEE}
}
@article{rui2023dilrs,
  title={DILRS: Domain-incremental learning for semantic segmentation in multi-source remote sensing data},
  author={Rui, Xue and Li, Ziqiang and Cao, Yang and Li, Ziyang and Song, Weiguo},
  journal={Remote Sensing},
  volume={15},
  number={10},
  pages={2541},
  year={2023},
  publisher={MDPI}
}
@article{oquab2023dinov2,
  title={Dinov2: Learning robust visual features without supervision},
  author={Oquab, Maxime and Darcet, Timoth{\'e}e and Moutakanni, Th{\'e}o and Vo, Huy and Szafraniec, Marc and Khalidov, Vasil and Fernandez, Pierre and Haziza, Daniel and Massa, Francisco and El-Nouby, Alaaeldin and others},
  journal={arXiv preprint arXiv:2304.07193},
  year={2023}
}

@inproceedings{boum2024continual,
  title={Continual learning in remote sensing: Leveraging foundation models and generative classifiers to mitigate forgetting},
  author={Boum, Marie-Ange and Herbin, St{\'e}phane and Fournier, Pierre and Lassalle, Pierre},
  booktitle={IGARSS 2024-2024 IEEE International Geoscience and Remote Sensing Symposium},
  pages={8535--8540},
  year={2024},
  organization={IEEE}
}

@article{chen2023saving,
  title={Saving 100x storage: Prototype replay for reconstructing training sample distribution in class-incremental semantic segmentation},
  author={Chen, Jinpeng and Cong, Runmin and Luo, Yuxuan and Ip, Horace and Kwong, Sam},
  journal={Advances in Neural Information Processing Systems},
  volume={36},
  pages={35988--35999},
  year={2023}}

@article{garcia2017review,
  title={A review on deep learning techniques applied to semantic segmentation},
  author={Garcia-Garcia, Alberto and Orts-Escolano, Sergio and Oprea, Sergiu and Villena-Martinez, Victor and Garcia-Rodriguez, Jose},
  journal={arXiv preprint arXiv:1704.06857},
  year={2017}
}
@article{neyshabur2020being,
  title={What is being transferred in transfer learning?},
  author={Neyshabur, Behnam and Sedghi, Hanie and Zhang, Chiyuan},
  journal={Advances in neural information processing systems},
  volume={33},
  pages={512--523},
  year={2020}
}

@article{harun2023overcoming,
  title={Overcoming the stability gap in continual learning},
  author={Harun, Md Yousuf and Kanan, Christopher},
  journal={arXiv preprint arXiv:2306.01904},
  year={2023}
}

@inproceedings{marsden2022continual,
  title={Continual unsupervised domain adaptation for semantic segmentation using a class-specific transfer},
  author={Marsden, Robert A and Wiewel, Felix and D{\"o}bler, Mario and Yang, Yang and Yang, Bin},
  booktitle={2022 International Joint Conference on Neural Networks (IJCNN)},
  pages={1--8},
  year={2022},
  organization={IEEE}
}
@article{ridnik2021imagenet,
  title={Imagenet-21k pretraining for the masses},
  author={Ridnik, Tal and Ben-Baruch, Emanuel and Noy, Asaf and Zelnik-Manor, Lihi},
  journal={arXiv preprint arXiv:2104.10972},
  year={2021}
}
@inproceedings{wang2022scalable,
  title={Scalable Learning with Incremental Probabilistic PCA},
  author={Wang, Boshi and Barbu, Adrian},
  booktitle={2022 IEEE International Conference on Big Data (Big Data)},
  pages={5615--5622},
  year={2022},
  organization={IEEE}
}
@article{yang2023continual,
  title={Continual learning with Bayesian model based on a fixed pre-trained feature extractor},
  author={Yang, Yang and Cui, Zhiying and Xu, Junjie and Zhong, Changhong and Zheng, Wei-Shi and Wang, Ruixuan},
  journal={Visual Intelligence},
  volume={1},
  number={1},
  pages={5},
  year={2023},
  publisher={Springer}
}
@article{lapacz2024exploring,
  title={Exploring the Stability Gap in Continual Learning: The Role of the Classification Head},
  author={{\L}apacz, Wojciech and Marczak, Daniel and Szatkowski, Filip and Trzci{\'n}ski, Tomasz},
  journal={arXiv preprint arXiv:2411.04723},
  year={2024}
}
@article{hess2023two,
  title={Two complementary perspectives to continual learning: Ask not only what to optimize, but also how},
  author={Hess, Timm and Tuytelaars, Tinne and van de Ven, Gido M},
  journal={arXiv preprint arXiv:2311.04898},
  year={2023}
}
@article{de2022continual,
  title={Continual evaluation for lifelong learning: Identifying the stability gap},
  author={De Lange, Matthias and van de Ven, Gido and Tuytelaars, Tinne},
  journal={arXiv preprint arXiv:2205.13452},
  year={2022}
}
@article{wistuba2023continual,
  title={Continual Learning with Low Rank Adaptation},
  author={Wistuba, Martin and Sivaprasad, Prabhu Teja and Balles, Lukas and Zappella, Giovanni},
  journal={arXiv preprint arXiv:2311.17601},
  year={2023}
}
@inproceedings{yuan2023power,
  title={On the power of foundation models},
  author={Yuan, Yang},
  booktitle={International Conference on Machine Learning},
  pages={40519--40530},
  year={2023},
  organization={PMLR}
}
@article{chaudhry2019tiny,
  title={On tiny episodic memories in continual learning},
  author={Chaudhry, Arslan and Rohrbach, Marcus and Elhoseiny, Mohamed and Ajanthan, Thalaiyasingam and Dokania, Puneet K and Torr, Philip HS and Ranzato, Marc'Aurelio},
  journal={arXiv preprint arXiv:1902.10486},
  year={2019}
}
@article{goodfellow2014generative,
  title={Generative adversarial nets},
  author={Goodfellow, Ian and Pouget-Abadie, Jean and Mirza, Mehdi and Xu, Bing and Warde-Farley, David and Ozair, Sherjil and Courville, Aaron and Bengio, Yoshua},
  journal={Advances in neural information processing systems},
  volume={27},
  year={2014}
}
@article{shin2017continual,
  title={Continual learning with deep generative replay},
  author={Shin, Hanul and Lee, Jung Kwon and Kim, Jaehong and Kim, Jiwon},
  journal={Advances in neural information processing systems},
  volume={30},
  year={2017}
}
@inproceedings{lesort2019generative,
  title={Generative models from the perspective of continual learning},
  author={Lesort, Timoth{\'e}e and Caselles-Dupr{\'e}, Hugo and Garcia-Ortiz, Michael and Stoian, Andrei and Filliat, David},
  booktitle={2019 International Joint Conference on Neural Networks (IJCNN)},
  pages={1--8},
  year={2019},
  organization={IEEE}
}
@article{aljundi2019online,
  title={Online Continual Learning with Maximal Interfered Retrieval. In< i> Advances in Neural Information Processing Systems (NeurIPS)},
  author={Aljundi, R and Belilovsky, E and Tuytelaars, T and Charlin, L and Caccia, M and Lin, M and Page-Caccia, L},
  journal={arXiv preprint arXiv:1908.04742},
  year={2019}
}
@article{aljundi2019gradient,
  title={Gradient based sample selection for online continual learning},
  author={Aljundi, Rahaf and Lin, Min and Goujaud, Baptiste and Bengio, Yoshua},
  journal={Advances in neural information processing systems},
  volume={32},
  year={2019}
}
@article{devlin2018bert,
  title={Bert: Pre-training of deep bidirectional transformers for language understanding},
  author={Devlin, Jacob and Chang, Ming-Wei and Lee, Kenton and Toutanova, Kristina},
  journal={arXiv preprint arXiv:1810.04805},
  year={2018}
}
@article{mehta2023empirical,
  title={An empirical investigation of the role of pre-training in lifelong learning},
  author={Mehta, Sanket Vaibhav and Patil, Darshan and Chandar, Sarath and Strubell, Emma},
  journal={Journal of Machine Learning Research},
  volume={24},
  number={214},
  pages={1--50},
  year={2023}
}
@inproceedings{mendieta2023towards,
  title={Towards Geospatial Foundation Models via Continual Pretraining},
  author={Mendieta, Mat{\'\i}as and Han, Boran and Shi, Xingjian and Zhu, Yi and Chen, Chen},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={16806--16816},
  year={2023}
}
@inproceedings{ramasesh2021effect,
  title={Effect of scale on catastrophic forgetting in neural networks},
  author={Ramasesh, Vinay Venkatesh and Lewkowycz, Aitor and Dyer, Ethan},
  booktitle={International Conference on Learning Representations},
  year={2021}
}
@inproceedings{alfarra2024simcs,
  title={SimCS: Simulation for domain incremental online continual segmentation},
  author={Alfarra, Motasem and Cai, Zhipeng and Bibi, Adel and Ghanem, Bernard and M{\"u}ller, Matthias},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={38},
  number={10},
  pages={10795--10803},
  year={2024}
}
@ARTICLE{10723740,
  author={Huang, Wubiao and Ding, Mingtao and Deng, Fei},
  journal={IEEE Transactions on Geoscience and Remote Sensing}, 
  title={Domain-Incremental Learning for Remote Sensing Semantic Segmentation With Multifeature Constraints in Graph Space}, 
  year={2024},volume={62},number={},pages={1-15},
  keywords={Remote sensing;Incremental learning;Semantic segmentation;Computational modeling;Semantics;Deep learning;Cognition;Electronics packaging;Adaptation models;Training;Cross attention;domain-incremental learning (DIL);graph space reasoning (GSR);remote sensing image;semantic segmentation},
  doi={10.1109/TGRS.2024.3481875}}

@article{parisi2019continual,
  title={Continual lifelong learning with neural networks: A review},
  author={Parisi, German I and Kemker, Ronald and Part, Jose L and Kanan, Christopher and Wermter, Stefan},
  journal={Neural networks},
  volume={113},
  pages={54--71},
  year={2019},
  publisher={Elsevier}
}
@article{caccia2021new,
  title={New insights on reducing abrupt representation change in online continual learning},
  author={Caccia, Lucas and Aljundi, Rahaf and Asadi, Nader and Tuytelaars, Tinne and Pineau, Joelle and Belilovsky, Eugene},
  journal={arXiv preprint arXiv:2104.05025},
  year={2021}
}

@article{driscoll2022representational,
  title={Representational drift: Emerging theories for continual learning and experimental future directions},
  author={Driscoll, Laura N and Duncker, Lea and Harvey, Christopher D},
  journal={Current Opinion in Neurobiology},
  volume={76},
  pages={102609},
  year={2022},
  publisher={Elsevier}
}

@inproceedings{gu2023preserving,
  title={Preserving Linear Separability in Continual Learning by Backward Feature Projection},
  author={Gu, Qiao and Shim, Dongsub and Shkurti, Florian},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={24286--24295},
  year={2023}
}
@article{shi2023unified,
  title={A Unified Approach to Domain Incremental Learning with Memory: Theory and Algorithm},
  author={Shi, Haizhou and Wang, Hao},
  journal={arXiv preprint arXiv:2310.12244},
  year={2023}
}
@article{tipping1999probabilistic,
  title={Probabilistic principal component analysis},
  author={Tipping, Michael E and Bishop, Christopher M},
  journal={Journal of the Royal Statistical Society Series B: Statistical Methodology},
  volume={61},
  number={3},
  pages={611--622},
  year={1999},
  publisher={Oxford University Press}
}
@InProceedings{Wang_2022_CVPR,
    author    = {Wang, Qin and Fink, Olga and Van Gool, Luc and Dai, Dengxin},
    title     = {Continual Test-Time Domain Adaptation},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2022},
    pages     = {7201-7211}
}
@inproceedings{liu_learning_2020,
	title = {Learning to {Adapt} to {Evolving} {Domains}},
	volume = {33},
	url = {https://proceedings.neurips.cc/paper_files/paper/2020/file/fd69dbe29f156a7ef876a40a94f65599-Paper.pdf},
	booktitle = {Advances in {Neural} {Information} {Processing} {Systems}},
	publisher = {Curran Associates, Inc.},
	author = {Liu, Hong and Long, Mingsheng and Wang, Jianmin and Wang, Yu},
	editor = {Larochelle, H. and Ranzato, M. and Hadsell, R. and Balcan, M. F. and Lin, H.},
	year = {2020},
	pages = {22338--22348},
}
@article{rostami2021lifelong,
  title={Lifelong domain adaptation via consolidated internal distribution},
  author={Rostami, Mohammad},
  journal={Advances in neural information processing systems},
  volume={34},
  pages={11172--11183},
  year={2021}
}
@article{wang2020continuously,
  title={Continuously indexed domain adaptation},
  author={Wang, Hao and He, Hao and Katabi, Dina},
  journal={arXiv preprint arXiv:2007.01807},
  year={2020}
}
@article{bobu2018adapting,
  title={Adapting to continuously shifting domains},
  author={Bobu, Andreea and Tzeng, Eric and Hoffman, Judy and Darrell, Trevor},
  year={2018}
}

@article{truong2022conda,
  title={Conda: Continual unsupervised domain adaptation learning in visual perception for self-driving cars},
  author={Truong, Thanh-Dat and Helton, Pierce and Moustafa, Ahmed and Cothren, Jackson David and Luu, Khoa},
  journal={arXiv preprint arXiv:2212.00621},
  year={2022}
}
@inproceedings{wulfmeier2018incremental,
  title={Incremental adversarial domain adaptation for continually changing environments},
  author={Wulfmeier, Markus and Bewley, Alex and Posner, Ingmar},
  booktitle={2018 IEEE International conference on robotics and automation (ICRA)},
  pages={4489--4495},
  year={2018},
  organization={IEEE}
}

@article{belouadah2021comprehensive,
  title={A comprehensive study of class incremental learning algorithms for visual tasks},
  author={Belouadah, Eden and Popescu, Adrian and Kanellos, Ioannis},
  journal={Neural Networks},
  volume={135},
  pages={38--54},
  year={2021},
  publisher={Elsevier}
}

@inproceedings{maggiori2017dataset,
  title={Can Semantic Labeling Methods Generalize to Any City? The Inria Aerial Image Labeling Benchmark},
  author={Maggiori, Emmanuel and Tarabalka, Yuliya and Charpiat, Guillaume and Alliez, Pierre},
  booktitle={IEEE International Geoscience and Remote Sensing Symposium (IGARSS)},
  year={2017},
  organization={IEEE}
}

@inproceedings{prabhu2020gdumb,
  title={Gdumb: A simple approach that questions our progress in continual learning},
  author={Prabhu, Ameya and Torr, Philip HS and Dokania, Puneet K},
  booktitle={Computer Vision--ECCV 2020: 16th European Conference, Glasgow, UK, August 23--28, 2020, Proceedings, Part II 16},
  pages={524--540},
  year={2020},
  organization={Springer}
}
@inproceedings{tsai2018learning,
  title={Learning to adapt structured output space for semantic segmentation},
  author={Tsai, Yi-Hsuan and Hung, Wei-Chih and Schulter, Samuel and Sohn, Kihyuk and Yang, Ming-Hsuan and Chandraker, Manmohan},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={7472--7481},
  year={2018}
}
@inproceedings{wang2019conditional,
  title={Conditional coupled generative adversarial networks for zero-shot domain adaptation},
  author={Wang, Jinghua and Jiang, Jianmin},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={3375--3384},
  year={2019}
}
@inproceedings{stan2021unsupervised,
  title={Unsupervised model adaptation for continual semantic segmentation},
  author={Stan, Serban and Rostami, Mohammad},
  booktitle={Proceedings of the AAAI conference on artificial intelligence},
  volume={35},
  number={3},
  pages={2593--2601},
  year={2021}
}

@misc{noauthor_sentinel-2_nodate,
	title = {Sentinel-2 - {Missions} - {Sentinel} {Online}},
	url = {https://copernicus.eu/missions/sentinel-2},
	language = {en-US},
	urldate = {2023-06-08},
	journal = {Sentinel Online},
	file = {Snapshot:/d/maboum/Zotero/storage/LF9FY9UK/sentinel-2.html:text/html},
}
@article{volpi2016dense,
  title={Dense semantic labeling of subdecimeter resolution images with convolutional neural networks},
  author={Volpi, Michele and Tuia, Devis},
  journal={IEEE Transactions on Geoscience and Remote Sensing},
  volume={55},
  number={2},
  pages={881--893},
  year={2016},
  publisher={IEEE}
}
@inproceedings{volpi2021continual,
  title={Continual adaptation of visual representations via domain randomization and meta-learning},
  author={Volpi, Riccardo and Larlus, Diane and Rogez, Gr{\'e}gory},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={4443--4453},
  year={2021}
}
@inproceedings{van2021class,
  title={Class-incremental learning with generative classifiers},
  author={Van De Ven, Gido M and Li, Zhe and Tolias, Andreas S},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={3611--3620},
  year={2021}
}
@article{imagenet15russakovsky,
    Author = {Olga Russakovsky and Jia Deng and Hao Su and Jonathan Krause and Sanjeev Satheesh and Sean Ma and Zhiheng Huang and Andrej Karpathy and Aditya Khosla and Michael Bernstein and Alexander C. Berg and Li Fei-Fei},
    Title = { {ImageNet Large Scale Visual Recognition Challenge} },
    Year = {2015},
    journal   = {International Journal of Computer Vision (IJCV)},
    doi = {10.1007/s11263-015-0816-y},
    volume={115},
    number={3},
    pages={211-252}
}
@article{long2018conditional,
  title={Conditional adversarial domain adaptation},
  author={Long, Mingsheng and Cao, Zhangjie and Wang, Jianmin and Jordan, Michael I},
  journal={Advances in neural information processing systems},
  volume={31},
  year={2018}
}

@inproceedings{saporta2022multi,
  title={Multi-head distillation for continual unsupervised domain adaptation in semantic segmentation},
  author={Saporta, Antoine and Douillard, Arthur and Vu, Tuan-Hung and P{\'e}rez, Patrick and Cord, Matthieu},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={3751--3760},
  year={2022}
}
@inproceedings{hoffman2014continuous,
  title={Continuous manifold based adaptation for evolving visual domains},
  author={Hoffman, Judy and Darrell, Trevor and Saenko, Kate},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={867--874},
  year={2014}
}
@inproceedings{simon2022generalizing,
  title={On generalizing beyond domains in cross-domain continual learning},
  author={Simon, Christian and Faraki, Masoud and Tsai, Yi-Hsuan and Yu, Xiang and Schulter, Samuel and Suh, Yumin and Harandi, Mehrtash and Chandraker, Manmohan},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={9265--9274},
  year={2022}
}
@article{kirillov2023segany,
  title={Segment Anything},
  author={Kirillov, Alexander and Mintun, Eric and Ravi, Nikhila and Mao, Hanzi and Rolland, Chloe and Gustafson, Laura and Xiao, Tete and Whitehead, Spencer and Berg, Alexander C. and Lo, Wan-Yen and Doll{\'a}r, Piotr and Girshick, Ross},
  journal={arXiv:2304.02643},
  year={2023}
}

@article{rvsa,
  author={Wang, Di and Zhang, Qiming and Xu, Yufei and Zhang, Jing and Du, Bo and Tao, Dacheng and Zhang, Liangpei},
  journal={IEEE Transactions on Geoscience and Remote Sensing}, 
  title={Advancing Plain Vision Transformer Toward Remote Sensing Foundation Model}, 
  year={2023},
  volume={61},
  number={},
  pages={1-15},
  doi={10.1109/TGRS.2022.3222818}
 }

@article{rsp,
  author={Wang, Di and Zhang, Jing and Du, Bo and Xia, Gui-Song and Tao, Dacheng},
  journal={IEEE Transactions on Geoscience and Remote Sensing}, 
  title={An Empirical Study of Remote Sensing Pretraining}, 
  year={2023},
  volume={61},
  number={},
  pages={1-20},
  doi={10.1109/TGRS.2022.3176603}
}
@article{wang2022advancing,
  title={Advancing plain vision transformer toward remote sensing foundation model},
  author={Wang, Di and Zhang, Qiming and Xu, Yufei and Zhang, Jing and Du, Bo and Tao, Dacheng and Zhang, Liangpei},
  journal={IEEE Transactions on Geoscience and Remote Sensing},
  volume={61},
  pages={1--15},
  year={2022},
  publisher={IEEE}
}

@inproceedings{kundu2020class,
  title={Class-incremental domain adaptation},
  author={Kundu, Jogendra Nath and Venkatesh, Rahul Mysore and Venkat, Naveen and Revanur, Ambareesh and Babu, R Venkatesh},
  booktitle={Computer Vision--ECCV 2020: 16th European Conference, Glasgow, UK, August 23--28, 2020, Proceedings, Part XIII 16},
  pages={53--69},
  year={2020},
  organization={Springer}
}
@article{khan2022transformers,
  title={Transformers in vision: A survey},
  author={Khan, Salman and Naseer, Muzammal and Hayat, Munawar and Zamir, Syed Waqas and Khan, Fahad Shahbaz and Shah, Mubarak},
  journal={ACM computing surveys (CSUR)},
  volume={54},
  number={10s},
  pages={1--41},
  year={2022},
  publisher={ACM New York, NY}
}
@inproceedings{caron2021emerging,
  title={Emerging Properties in Self-Supervised Vision Transformers},
  author={Caron, Mathilde and Touvron, Hugo and Misra, Ishan and J\'egou, Herv\'e  and Mairal, Julien and Bojanowski, Piotr and Joulin, Armand},
  booktitle={Proceedings of the International Conference on Computer Vision (ICCV)},
  year={2021}
}
@inproceedings{tang2021gradient,
  title={Gradient regularized contrastive learning for continual domain adaptation},
  author={Tang, Shixiang and Su, Peng and Chen, Dapeng and Ouyang, Wanli},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={35},
  number={3},
  pages={2665--2673},
  year={2021}
}
@article{taufique2021conda,
  title={ConDA: Continual unsupervised domain adaptation},
  author={Taufique, Abu Md Niamul and Jahan, Chowdhury Sadman and Savakis, Andreas},
  journal={arXiv preprint arXiv:2103.11056},
  year={2021}
}
@inproceedings{taufique2022unsupervised,
  title={Unsupervised continual learning for gradually varying domains},
  author={Taufique, Abu Md Niamul and Jahan, Chowdhury Sadman and Savakis, Andreas},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={3740--3750},
  year={2022}
}
@article{kim2020continual,
  title={Continual unsupervised domain adaptation for semantic segmentation},
  author={Kim, Joonhyuk and Yoo, Sahng-Min and Park, Gyeong-Moon and Kim, Jong-Hwan},
  journal={arXiv preprint arXiv:2010.09236},
  year={2020}
}

@inproceedings{wu2019ace,
  title={Ace: Adapting to changing environments for semantic segmentation},
  author={Wu, Zuxuan and Wang, Xin and Gonzalez, Joseph E and Goldstein, Tom and Davis, Larry S},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={2121--2130},
  year={2019}
}
@inproceedings{garg2022multi,
  title={Multi-domain incremental learning for semantic segmentation},
  author={Garg, Prachi and Saluja, Rohit and Balasubramanian, Vineeth N and Arora, Chetan and Subramanian, Anbumani and Jawahar, CV},
  booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision},
  pages={761--771},
  year={2022}
}
@inproceedings{mirza2022efficient,
  title={An efficient domain-incremental learning approach to drive in all weather conditions},
  author={Mirza, M Jehanzeb and Masana, Marc and Possegger, Horst and Bischof, Horst},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={3001--3011},
  year={2022}
}
@inproceedings{kalb2023principles,
  title={Principles of Forgetting in Domain-Incremental Semantic Segmentation in Adverse Weather Conditions},
  author={Kalb, Tobias and Beyerer, J{\"u}rgen},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={19508--19518},
  year={2023}
}
@inproceedings{audebert2017semantic,
  title={Semantic segmentation of earth observation data using multimodal and multi-scale deep networks},
  author={Audebert, Nicolas and Le Saux, Bertrand and Lef{\`e}vre, S{\'e}bastien},
  booktitle={Computer Vision--ACCV 2016: 13th Asian Conference on Computer Vision, Taipei, Taiwan, November 20-24, 2016, Revised Selected Papers, Part I 13},
  pages={180--196},
  year={2017},
  organization={Springer}
}
@inproceedings{ronneberger2015u,
  title={U-net: Convolutional networks for biomedical image segmentation},
  author={Ronneberger, Olaf and Fischer, Philipp and Brox, Thomas},
  booktitle={Medical Image Computing and Computer-Assisted Intervention--MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18},
  pages={234--241},
  year={2015},
  organization={Springer}
}
@inproceedings{maggiori2017can,
  title={Can semantic labeling methods generalize to any city? the inria aerial image labeling benchmark},
  author={Maggiori, Emmanuel and Tarabalka, Yuliya and Charpiat, Guillaume and Alliez, Pierre},
  booktitle={2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)},
  pages={3226--3229},
  year={2017},
  organization={IEEE}
}
@article{goodfellow2013empirical,
  title={An empirical investigation of catastrophic forgetting in gradient-based neural networks},
  author={Goodfellow, Ian J and Mirza, Mehdi and Xiao, Da and Courville, Aaron and Bengio, Yoshua},
  journal={arXiv preprint arXiv:1312.6211},
  year={2013}
}
@inproceedings{kalb2021continual,
  title={Continual learning for class-and domain-incremental semantic segmentation},
  author={Kalb, Tobias and Roschani, Masoud and Ruf, Miriam and Beyerer, J{\"u}rgen},
  booktitle={2021 IEEE Intelligent Vehicles Symposium (IV)},
  pages={1345--1351},
  year={2021},
  organization={IEEE}
}
@article{van2022three,
  title={Three types of incremental learning},
  author={van de Ven, Gido M and Tuytelaars, Tinne and Tolias, Andreas S},
  journal={Nature Machine Intelligence},
  pages={1--13},
  year={2022},
  publisher={Nature Publishing Group UK London}
}

@inproceedings{castro2018end,
  title={End-to-end incremental learning},
  author={Castro, Francisco M and Mar{\'\i}n-Jim{\'e}nez, Manuel J and Guil, Nicol{\'a}s and Schmid, Cordelia and Alahari, Karteek},
  booktitle={Proceedings of the European conference on computer vision (ECCV)},
  pages={233--248},
  year={2018}
}
@inproceedings{aljundi2018memory,
  title={Memory aware synapses: Learning what (not) to forget},
  author={Aljundi, Rahaf and Babiloni, Francesca and Elhoseiny, Mohamed and Rohrbach, Marcus and Tuytelaars, Tinne},
  booktitle={Proceedings of the European conference on computer vision (ECCV)},
  pages={139--154},
  year={2018}
}

@inproceedings{rebuffi2017icarl,
  title={icarl: Incremental classifier and representation learning},
  author={Rebuffi, Sylvestre-Alvise and Kolesnikov, Alexander and Sperl, Georg and Lampert, Christoph H},
  booktitle={Proceedings of the IEEE conference on Computer Vision and Pattern Recognition},
  pages={2001--2010},
  year={2017}
}
@article{7272047,
  title        = {Deep Learning Based Feature Selection for Remote Sensing Scene Classification},
  author       = {Zou, Qin and Ni, Lihao and Zhang, Tong and Wang, Qian},
  year         = 2015,
  journal      = {IEEE Geoscience and Remote Sensing Letters},
  volume       = 12,
  number       = 11,
  pages        = {2321--2325},
  doi          = {10.1109/LGRS.2015.2475299}
}
@article{Long2021DiRS,
title={On Creating Benchmark Dataset for Aerial Image Interpretation: Reviews, Guidances and Million-AID},
author={Yang Long and Gui-Song Xia and Shengyang Li and Wen Yang and Michael Ying Yang and Xiao Xiang Zhu and Liangpei Zhang and Deren Li},
journal={IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
year={2021},
volume={14},
pages={4205-4230}
}
@inproceedings{douillard2022dytox,
  title = {{{DyTox}}: {{Transformers}} for {{Continual Learning With DYnamic TOken eXpansion}}},
  shorttitle = {{{DyTox}}},
  booktitle = {Proceedings of the {{IEEE}}/{{CVF Conference}} on {{Computer Vision}} and {{Pattern Recognition}}},
  author = {Douillard, Arthur and Ram{\'e}, Alexandre and Couairon, Guillaume and Cord, Matthieu},
  year = {2022},
  pages = {9285--9295},
  urldate = {2023-04-17},
  langid = {english},
  file = {/home/stephane/Zotero/storage/QIQRMV35/Douillard et al. - 2022 - DyTox Transformers for Continual Learning With DY.pdf}
}
@inproceedings{wang2022learning,
  title={Learning to prompt for continual learning},
  author={Wang, Zifeng and Zhang, Zizhao and Lee, Chen-Yu and Zhang, Han and Sun, Ruoxi and Ren, Xiaoqi and Su, Guolong and Perot, Vincent and Dy, Jennifer and Pfister, Tomas},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={139--149},
  year={2022}
}
@inproceedings{ulusoy2005generative,
  title={Generative versus discriminative methods for object recognition},
  author={Ulusoy, Ilkay and Bishop, Christopher M},
  booktitle={2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)},
  volume={2},
  pages={258--265},
  year={2005},
  organization={IEEE}
}

@inproceedings{ayush2021geographyaware,
  title = {Geography-{{Aware Self-Supervised Learning}}},
  booktitle = {Proceedings of the {{IEEE}}/{{CVF International Conference}} on {{Computer Vision}}},
  author = {Ayush, Kumar and Uzkent, Burak and Meng, Chenlin and Tanmay, Kumar and Burke, Marshall and Lobell, David and Ermon, Stefano},
  year = {2021},
  pages = {10181--10190},
  urldate = {2023-05-08},
  langid = {english},
  file = {C:\Users\steph\Zotero\storage\P5PBIAP9\Ayush et al. - 2021 - Geography-Aware Self-Supervised Learning.pdf}
}

@article{belouadah2021comprehensive,
  title = {A Comprehensive Study of Class Incremental Learning Algorithms for Visual Tasks},
  author = {Belouadah, Eden and Popescu, Adrian and Kanellos, Ioannis},
  year = {2021},
  month = mar,
  journal = {Neural Networks},
  volume = {135},
  pages = {38--54},
  issn = {0893-6080},
  doi = {10.1016/j.neunet.2020.12.003},
  urldate = {2022-03-22},
  abstract = {The ability of artificial agents to increment their capabilities when confronted with new data is an open challenge in artificial intelligence. The main challenge faced in such cases is catastrophic forgetting, i.e., the tendency of neural networks to underfit past data when new ones are ingested. A first group of approaches tackles forgetting by increasing deep model capacity to accommodate new knowledge. A second type of approaches fix the deep model size and introduce a mechanism whose objective is to ensure a good compromise between stability and plasticity of the model. While the first type of algorithms were compared thoroughly, this is not the case for methods which exploit a fixed size model. Here, we focus on the latter, place them in a common conceptual and experimental framework and propose the following contributions: (1) define six desirable properties of incremental learning algorithms and analyze them according to these properties, (2) introduce a unified formalization of the class-incremental learning problem, (3) propose a common evaluation framework which is more thorough than existing ones in terms of number of datasets, size of datasets, size of bounded memory and number of incremental states, (4) investigate the usefulness of herding for past exemplars selection, (5) provide experimental evidence that it is possible to obtain competitive performance without the use of knowledge distillation to tackle catastrophic forgetting and (6) facilitate reproducibility by integrating all tested methods in a common open-source repository. The main experimental finding is that none of the existing algorithms achieves the best results in all evaluated settings. Important differences arise notably if a bounded memory of past classes is allowed or not.},
  langid = {english},
  keywords = {Catastrophic forgetting,Convolutional neural networks,Image classification,Imbalanced learning,Incremental learning},
  file = {C:\Users\steph\Zotero\storage\G49CA8C3\Belouadah et al. - 2021 - A comprehensive study of class incremental learnin.pdf}
}

@misc{benigmim2023collaborating,
  title = {Collaborating {{Foundation}} Models for {{Domain Generalized Semantic Segmentation}}},
  author = {Benigmim, Yasser and Roy, Subhankar and Essid, Slim and Kalogeiton, Vicky and Lathuili{\`e}re, St{\'e}phane},
  year = {2023},
  month = dec,
  number = {arXiv:2312.09788},
  eprint = {2312.09788},
  primaryclass = {cs},
  publisher = {arXiv},
  doi = {10.48550/arXiv.2312.09788},
  urldate = {2024-02-01},
  abstract = {Domain Generalized Semantic Segmentation (DGSS) deals with training a model on a labeled source domain with the aim of generalizing to unseen domains during inference. Existing DGSS methods typically effectuate robust features by means of Domain Randomization (DR). Such an approach is often limited as it can only account for style diversification and not content. In this work, we take an orthogonal approach to DGSS and propose to use an assembly of CoLlaborative FOUndation models for Domain Generalized Semantic Segmentation (CLOUDS). In detail, CLOUDS is a framework that integrates FMs of various kinds: (i) CLIP backbone for its robust feature representation, (ii) generative models to diversify the content, thereby covering various modes of the possible target distribution, and (iii) Segment Anything Model (SAM) for iteratively refining the predictions of the segmentation model. Extensive experiments show that our CLOUDS excels in adapting from synthetic to real DGSS benchmarks and under varying weather conditions, notably outperforming prior methods by 5.6\% and 6.7\% on averaged miou, respectively. The code is available at : https://github.com/yasserben/CLOUDS},
  archiveprefix = {arXiv},
  keywords = {Computer Science - Artificial Intelligence,Computer Science - Computer Vision and Pattern Recognition,Computer Science - Machine Learning},
  file = {C\:\\Users\\steph\\Zotero\\storage\\WIXSSGQ7\\Benigmim et al. - 2023 - Collaborating Foundation models for Domain General.pdf;C\:\\Users\\steph\\Zotero\\storage\\AH2W9F6W\\2312.html}
}

@inproceedings{benigmim2024collaborating,
  title = {Collaborating {{Foundation Models}} for {{Domain Generalized Semantic Segmentation}}},
  booktitle = {Proceedings of the {{IEEE}}/{{CVF Conference}} on {{Computer Vision}} and {{Pattern Recognition}}},
  author = {Benigmim, Yasser and Roy, Subhankar and Essid, Slim and Kalogeiton, Vicky and Lathuili{\`e}re, St{\'e}phane},
  year = {2024},
  pages = {3108--3119},
  urldate = {2024-06-25},
  langid = {english},
  file = {C:\Users\steph\Zotero\storage\8LYQCEKB\Benigmim et al. - 2024 - Collaborating Foundation Models for Domain General.pdf}
}

@inproceedings{benigmim2024collaboratinga,
  title = {Collaborating {{Foundation Models}} for {{Domain Generalized Semantic Segmentation}}},
  booktitle = {Proceedings of the {{IEEE}}/{{CVF Conference}} on {{Computer Vision}} and {{Pattern Recognition}}},
  author = {Benigmim, Yasser and Roy, Subhankar and Essid, Slim and Kalogeiton, Vicky and Lathuili{\`e}re, St{\'e}phane},
  year = {2024},
  pages = {3108--3119},
  urldate = {2024-08-31},
  langid = {english},
  file = {C:\Users\steph\Zotero\storage\XTLG37JD\Benigmim et al. - 2024 - Collaborating Foundation Models for Domain General.pdf}
}

@inproceedings{brendel2022approximating,
  title = {Approximating {{CNNs}} with {{Bag-of-local-Features}} Models Works Surprisingly Well on {{ImageNet}}},
  booktitle = {International {{Conference}} on {{Learning Representations}}},
  author = {Brendel, Wieland and Bethge, Matthias},
  year = {2022},
  month = feb,
  urldate = {2022-12-05},
  abstract = {Deep Neural Networks (DNNs) excel on many complex perceptual tasks but it has proven notoriously difficult to understand how they reach their decisions. We here introduce a high-performance DNN architecture on ImageNet whose decisions are considerably easier to explain. Our model, a simple variant of the ResNet-50 architecture called BagNet, classifies an image based on the occurrences of small local image features without taking into account their spatial ordering. This strategy is closely related to the bag-of-feature (BoF) models popular before the onset of deep learning and reaches a surprisingly high accuracy on ImageNet (87.6\% top-5 for 32 x 32 px features and Alexnet performance for 16 x16 px features). The constraint on local features makes it straight-forward to analyse how exactly each part of the image influences the classification. Furthermore, the BagNets behave similar to state-of-the art deep neural networks such as VGG-16, ResNet-152 or DenseNet-169 in terms of feature sensitivity, error distribution and interactions between image parts. This suggests that the improvements of DNNs over previous bag-of-feature classifiers in the last few years is mostly achieved by better fine-tuning rather than by qualitatively different decision strategies.},
  langid = {english},
  file = {C:\Users\steph\Zotero\storage\KAL3BFGW\Brendel et Bethge - 2022 - Approximating CNNs with Bag-of-local-Features mode.pdf}
}

@misc{broni-bediako2023realtime,
  title = {Real-{{Time Semantic Segmentation}}: {{A Brief Survey}} \& {{Comparative Study}} in {{Remote Sensing}}},
  shorttitle = {Real-{{Time Semantic Segmentation}}},
  author = {{Broni-Bediako}, Clifford and Xia, Junshi and Yokoya, Naoto},
  year = {2023},
  month = sep,
  number = {arXiv:2309.06047},
  eprint = {2309.06047},
  primaryclass = {cs},
  publisher = {arXiv},
  urldate = {2023-09-17},
  abstract = {Real-time semantic segmentation of remote sensing imagery is a challenging task that requires a trade-off between effectiveness and efficiency. It has many applications including tracking forest fires, detecting changes in land use and land cover, crop health monitoring, and so on. With the success of efficient deep learning methods (i.e., efficient deep neural networks) for real-time semantic segmentation in computer vision, researchers have adopted these efficient deep neural networks in remote sensing image analysis. This paper begins with a summary of the fundamental compression methods for designing efficient deep neural networks and provides a brief but comprehensive survey, outlining the recent developments in real-time semantic segmentation of remote sensing imagery. We examine several seminal efficient deep learning methods, placing them in a taxonomy based on the network architecture design approach. Furthermore, we evaluate the quality and efficiency of some existing efficient deep neural networks on a publicly available remote sensing semantic segmentation benchmark dataset, the OpenEarthMap. The experimental results of an extensive comparative study demonstrate that most of the existing efficient deep neural networks have good segmentation quality, but they suffer low inference speed (i.e., high latency rate), which may limit their capability of deployment in real-time applications of remote sensing image segmentation. We provide some insights into the current trend and future research directions for realtime semantic segmentation of remote sensing imagery.},
  archiveprefix = {arXiv},
  langid = {english},
  keywords = {Computer Science - Computer Vision and Pattern Recognition},
  file = {C:\Users\steph\Zotero\storage\GIA6QNR5\Broni-Bediako et al. - 2023 - Real-Time Semantic Segmentation A Brief Survey & .pdf}
}

@misc{broni-bediako2024generalized,
  title = {Generalized {{Few-Shot Semantic Segmentation}} in {{Remote Sensing}}: {{Challenge}} and {{Benchmark}}},
  shorttitle = {Generalized {{Few-Shot Semantic Segmentation}} in {{Remote Sensing}}},
  author = {{Broni-Bediako}, Clifford and Xia, Junshi and Song, Jian and Chen, Hongruixuan and Siam, Mennatullah and Yokoya, Naoto},
  year = {2024},
  month = sep,
  number = {arXiv:2409.11227},
  eprint = {2409.11227},
  primaryclass = {cs},
  publisher = {arXiv},
  doi = {10.48550/arXiv.2409.11227},
  urldate = {2024-09-18},
  abstract = {Learning with limited labelled data is a challenging problem in various applications, including remote sensing. Few-shot semantic segmentation is one approach that can encourage deep learning models to learn from few labelled examples for novel classes not seen during the training. The generalized few-shot segmentation setting has an additional challenge which encourages models not only to adapt to the novel classes but also to maintain strong performance on the training base classes. While previous datasets and benchmarks discussed the few-shot segmentation setting in remote sensing, we are the first to propose a generalized few-shot segmentation benchmark for remote sensing. The generalized setting is more realistic and challenging, which necessitates exploring it within the remote sensing context. We release the dataset augmenting OpenEarthMap with additional classes labelled for the generalized few-shot evaluation setting. The dataset is released during the OpenEarthMap land cover mapping generalized few-shot challenge in the L3D-IVU workshop in conjunction with CVPR 2024. In this work, we summarize the dataset and challenge details in addition to providing the benchmark results on the two phases of the challenge for the validation and test sets.},
  archiveprefix = {arXiv},
  keywords = {Computer Science - Computer Vision and Pattern Recognition},
  file = {C\:\\Users\\steph\\Zotero\\storage\\PBFLWUGS\\Broni-Bediako et al. - 2024 - Generalized Few-Shot Semantic Segmentation in Remo.pdf;C\:\\Users\\steph\\Zotero\\storage\\M2U8MA4E\\2409.html}
}

@misc{cha2024billionscale,
  title = {A {{Billion-scale Foundation Model}} for {{Remote Sensing Images}}},
  author = {Cha, Keumgang and Seo, Junghoon and Lee, Taekyung},
  year = {2024},
  month = may,
  number = {arXiv:2304.05215},
  eprint = {2304.05215},
  primaryclass = {cs},
  publisher = {arXiv},
  doi = {10.48550/arXiv.2304.05215},
  urldate = {2024-07-24},
  abstract = {As the potential of foundation models in visual tasks has garnered significant attention, pretraining these models before downstream tasks has become a crucial step. The three key factors in pretraining foundation models are the pretraining method, the size of the pretraining dataset, and the number of model parameters. Recently, research in the remote sensing field has focused primarily on the pretraining method and the size of the dataset, with limited emphasis on the number of model parameters. This paper addresses this gap by examining the effect of increasing the number of model parameters on the performance of foundation models in downstream tasks such as rotated object detection and semantic segmentation. We pretrained foundation models with varying numbers of parameters, including 86M, 605.26M, 1.3B, and 2.4B, to determine whether performance in downstream tasks improved with an increase in parameters. To the best of our knowledge, this is the first billion-scale foundation model in the remote sensing field. Furthermore, we propose an effective method for scaling up and fine-tuning a vision transformer in the remote sensing field. To evaluate general performance in downstream tasks, we employed the DOTA v2.0 and DIOR-R benchmark datasets for rotated object detection, and the Potsdam and LoveDA datasets for semantic segmentation. Experimental results demonstrated that, across all benchmark datasets and downstream tasks, the performance of the foundation models and data efficiency improved as the number of parameters increased. Moreover, our models achieve the state-of-the-art performance on several datasets including DIOR-R, Postdam, and LoveDA.},
  archiveprefix = {arXiv},
  keywords = {Computer Science - Artificial Intelligence,Computer Science - Computer Vision and Pattern Recognition,Computer Science - Machine Learning},
  file = {C:\Users\steph\Zotero\storage\WFZGZA2H\Cha et al. - 2024 - A Billion-scale Foundation Model for Remote Sensin.pdf}
}

@misc{chen2023rsprompter,
  title = {{{RSPrompter}}: {{Learning}} to {{Prompt}} for {{Remote Sensing Instance Segmentation}} Based on {{Visual Foundation Model}}},
  shorttitle = {{{RSPrompter}}},
  author = {Chen, Keyan and Liu, Chenyang and Chen, Hao and Zhang, Haotian and Li, Wenyuan and Zou, Zhengxia and Shi, Zhenwei},
  year = {2023},
  month = jun,
  number = {arXiv:2306.16269},
  eprint = {2306.16269},
  primaryclass = {cs},
  publisher = {arXiv},
  doi = {10.48550/arXiv.2306.16269},
  urldate = {2023-09-08},
  abstract = {Leveraging vast training data (SA-1B), the foundation Segment Anything Model (SAM) proposed by Meta AI Research exhibits remarkable generalization and zero-shot capabilities. Nonetheless, as a category-agnostic instance segmentation method, SAM heavily depends on prior manual guidance involving points, boxes, and coarse-grained masks. Additionally, its performance on remote sensing image segmentation tasks has yet to be fully explored and demonstrated. In this paper, we consider designing an automated instance segmentation approach for remote sensing images based on the SAM foundation model, incorporating semantic category information. Inspired by prompt learning, we propose a method to learn the generation of appropriate prompts for SAM input. This enables SAM to produce semantically discernible segmentation results for remote sensing images, which we refer to as RSPrompter. We also suggest several ongoing derivatives for instance segmentation tasks, based on recent developments in the SAM community, and compare their performance with RSPrompter. Extensive experimental results on the WHU building, NWPU VHR-10, and SSDD datasets validate the efficacy of our proposed method. Our code is accessible at {\textbackslash}url\{https://kyanchen.github.io/RSPrompter\}.},
  archiveprefix = {arXiv},
  keywords = {Computer Science - Computer Vision and Pattern Recognition},
  file = {C\:\\Users\\steph\\Zotero\\storage\\QXCXARS2\\Chen et al. - 2023 - RSPrompter Learning to Prompt for Remote Sensing .pdf;C\:\\Users\\steph\\Zotero\\storage\\J2EYYGQM\\2306.html}
}

@article{chen2024rsprompter,
  title = {{{RSPrompter}}: {{Learning}} to {{Prompt}} for {{Remote Sensing Instance Segmentation Based}} on {{Visual Foundation Model}}},
  shorttitle = {{{RSPrompter}}},
  author = {Chen, Keyan and Liu, Chenyang and Chen, Hao and Zhang, Haotian and Li, Wenyuan and Zou, Zhengxia and Shi, Zhenwei},
  year = {2024},
  journal = {IEEE Transactions on Geoscience and Remote Sensing},
  volume = {62},
  pages = {1--17},
  issn = {1558-0644},
  doi = {10.1109/TGRS.2024.3356074},
  urldate = {2024-09-20},
  abstract = {Leveraging the extensive training data from SA-1B, the segment anything model (SAM) demonstrates remarkable generalization and zero-shot capabilities. However, as a category-agnostic instance segmentation method, SAM heavily relies on prior manual guidance, including points, boxes, and coarse-grained masks. Furthermore, its performance in remote sensing image segmentation tasks remains largely unexplored and unproven. In this article, we aim to develop an automated instance segmentation approach for remote sensing images based on the foundational SAM model and incorporating semantic category information. Drawing inspiration from prompt learning, we propose a method to learn the generation of appropriate prompts for SAM. This enables SAM to produce semantically discernible segmentation results for remote sensing images, a concept that we have termed RSPrompter. We also propose several ongoing derivatives for instance segmentation tasks, drawing on recent advancements within the SAM community, and compare their performance with RSPrompter. Extensive experimental results, derived from the WHU building dataset, the NWPU VHR-10 dataset, and the SAR Ship Detection Dataset (SSDD) dataset, validate the effectiveness of our proposed method. The code for our method is publicly available at https://kychen.me/RSPrompter.},
  keywords = {Decoding,Detectors,Foundation model,Image segmentation,instance segmentation,Instance segmentation,prompt learning,Remote sensing,remote sensing images,segment anything model (SAM),Sensors,Task analysis},
  file = {C\:\\Users\\steph\\Zotero\\storage\\LBRG4MT8\\Chen et al. - 2024 - RSPrompter Learning to Prompt for Remote Sensing .pdf;C\:\\Users\\steph\\Zotero\\storage\\BR7SJK7P\\10409216.html}
}

@article{cheng2021maskedattention,
  title = {Masked-Attention {{Mask Transformer}} for {{Universal Image Segmentation}}},
  author = {Cheng, Bowen and Misra, Ishan and Schwing, Alexander G. and Kirillov, Alexander and Girdhar, Rohit},
  year = {2021},
  month = dec,
  journal = {arXiv:2112.01527 [cs]},
  eprint = {2112.01527},
  primaryclass = {cs},
  urldate = {2022-01-18},
  abstract = {Image segmentation is about grouping pixels with different semantics, e.g., category or instance membership, where each choice of semantics defines a task. While only the semantics of each task differ, current research focuses on designing specialized architectures for each task. We present Masked-attention Mask Transformer (Mask2Former), a new architecture capable of addressing any image segmentation task (panoptic, instance or semantic). Its key components include masked attention, which extracts localized features by constraining cross-attention within predicted mask regions. In addition to reducing the research effort by at least three times, it outperforms the best specialized architectures by a significant margin on four popular datasets. Most notably, Mask2Former sets a new state-of-the-art for panoptic segmentation (57.8 PQ on COCO), instance segmentation (50.1 AP on COCO) and semantic segmentation (57.7 mIoU on ADE20K).},
  archiveprefix = {arXiv},
  keywords = {Computer Science - Artificial Intelligence,Computer Science - Computer Vision and Pattern Recognition,Computer Science - Machine Learning},
  file = {C\:\\Users\\steph\\Zotero\\storage\\XX9DVD3X\\Cheng et al. - 2021 - Masked-attention Mask Transformer for Universal Im.pdf;C\:\\Users\\steph\\Zotero\\storage\\I4XNS5XF\\2112.html}
}

@misc{csurka2017domain,
  title = {Domain {{Adaptation}} for {{Visual Applications}}: {{A Comprehensive Survey}}},
  shorttitle = {Domain {{Adaptation}} for {{Visual Applications}}},
  author = {Csurka, Gabriela},
  year = {2017},
  month = mar,
  number = {arXiv:1702.05374},
  eprint = {1702.05374},
  primaryclass = {cs},
  publisher = {arXiv},
  doi = {10.48550/arXiv.1702.05374},
  urldate = {2022-10-21},
  abstract = {The aim of this paper is to give an overview of domain adaptation and transfer learning with a specific view on visual applications. After a general motivation, we first position domain adaptation in the larger transfer learning problem. Second, we try to address and analyze briefly the state-of-the-art methods for different types of scenarios, first describing the historical shallow methods, addressing both the homogeneous and the heterogeneous domain adaptation methods. Third, we discuss the effect of the success of deep convolutional architectures which led to new type of domain adaptation methods that integrate the adaptation within the deep architecture. Fourth, we overview the methods that go beyond image categorization, such as object detection or image segmentation, video analyses or learning visual attributes. Finally, we conclude the paper with a section where we relate domain adaptation to other machine learning solutions.},
  archiveprefix = {arXiv},
  keywords = {Computer Science - Computer Vision and Pattern Recognition},
  file = {C\:\\Users\\steph\\Zotero\\storage\\WK7PYY4P\\Csurka - 2017 - Domain Adaptation for Visual Applications A Compr.pdf;C\:\\Users\\steph\\Zotero\\storage\\B77YCLBI\\1702.html}
}

@inproceedings{elsayed2023addressing,
  title = {Addressing {{Loss}} of {{Plasticity}} and {{Catastrophic Forgetting}} in {{Continual Learning}}},
  booktitle = {The {{Twelfth International Conference}} on {{Learning Representations}}},
  author = {Elsayed, Mohamed and Mahmood, A. Rupam},
  year = {2023},
  month = oct,
  urldate = {2025-02-23},
  abstract = {Deep representation learning methods struggle with continual learning, suffering from both catastrophic forgetting of useful units and loss of plasticity, often due to rigid and unuseful units. While many methods address these two issues separately, only a few currently deal with both simultaneously. In this paper, we introduce Utility-based Perturbed Gradient Descent (UPGD) as a novel approach for the continual learning of representations. UPGD combines gradient updates with perturbations, where it applies smaller modifications to more useful units, protecting them from forgetting, and larger modifications to less useful units, rejuvenating their plasticity. We use a challenging streaming learning setup where continual learning problems have hundreds of non-stationarities and unknown task boundaries. We show that many existing methods suffer from at least one of the issues, predominantly manifested by their decreasing accuracy over tasks. On the other hand, UPGD continues to improve performance and surpasses or is competitive with all methods in all problems. Finally, in extended reinforcement learning experiments with PPO, we show that while Adam exhibits a performance drop after initial learning, UPGD avoids it by addressing both continual learning issues.},
  langid = {english},
  file = {C:\Users\steph\Zotero\storage\TS9RTM7Z\Elsayed et Mahmood - 2023 - Addressing Loss of Plasticity and Catastrophic Forgetting in Continual Learning.pdf}
}

@article{gao2024attention,
  title = {Attention {{Prompt-Driven Source-Free Adaptation}} for {{Remote Sensing Images Semantic Segmentation}}},
  author = {Gao, Kuiliang and You, Xiong and Li, Ke and Chen, Lingyu and Lei, Juan and Zuo, Xibing},
  year = {2024},
  journal = {IEEE Geoscience and Remote Sensing Letters},
  volume = {21},
  pages = {1--5},
  issn = {1558-0571},
  doi = {10.1109/LGRS.2024.3422805},
  urldate = {2024-09-16},
  abstract = {Recently, remote sensing images (RSIs) domain adaptation segmentation has been extensively studied. However, existing methods generally assume that source RSIs must be available, which is obviously an overly demanding condition and will increase unnecessary costs in practice. To this end, this letter takes the lead in exploring RSIs source-free adaptation segmentation, where only the offline model pretrained on the source domain and target RSIs are available. A novel method featuring prompt learning and vision foundation models is proposed, and the novelty design includes two aspects. First, to better adapt the general-purpose knowledge in the foundation model to different target RSIs, an attention-guided prompt tuning strategy is proposed, which can dynamically steer the knowledge at different layers and positions through prompts with different weights. Second, a feature alignment strategy with similarity distance is proposed for source-free domain adaptation by taking full advantage of the representation ability of the foundation model and the flexibility of prompt learning. Extensive experiments indicate that the performance of the proposed method is significantly superior to that of existing methods. Specifically, the mIoU of target RSIs has been improved by at least 3.14\% 4.18\%.},
  keywords = {Adaptation models,Foundation models,prompt learning,Prototypes,Remote sensing,remote sensing images (RSIs),semantic segmentation,source-free adaptation,Task analysis,Training,Transformers,Tuning},
  file = {C\:\\Users\\steph\\Zotero\\storage\\2FMZFRSS\\Gao et al. - 2024 - Attention Prompt-Driven Source-Free Adaptation for.pdf;C\:\\Users\\steph\\Zotero\\storage\\4XBENAMA\\10583928.html}
}

@inproceedings{gao2024enrich,
  title = {Enrich {{Distill}} and {{Fuse}}: {{Generalized Few-Shot Semantic Segmentation}} in {{Remote Sensing Leveraging Foundation Model}}'s {{Assistance}}},
  shorttitle = {Enrich {{Distill}} and {{Fuse}}},
  booktitle = {Proceedings of the {{IEEE}}/{{CVF Conference}} on {{Computer Vision}} and {{Pattern Recognition}}},
  author = {Gao, Tianyi and Ao, Wei and Wang, Xing-Ao and Zhao, Yuanhao and Ma, Ping and Xie, Mengjie and Fu, Hang and Ren, Jinchang and Gao, Zhi},
  year = {2024},
  pages = {2771--2780},
  urldate = {2024-09-18},
  langid = {english},
  file = {C:\Users\steph\Zotero\storage\VCF6A5FB\Gao et al. - 2024 - Enrich Distill and Fuse Generalized Few-Shot Sema.pdf}
}

@misc{gong2022oneshot,
  title = {One-{{Shot Domain Adaptive}} and {{Generalizable Semantic Segmentation}} with {{Class-Aware Cross-Domain Transformers}}},
  author = {Gong, Rui and Wang, Qin and Dai, Dengxin and Van Gool, Luc},
  year = {2022},
  month = dec,
  number = {arXiv:2212.07292},
  eprint = {2212.07292},
  primaryclass = {cs},
  publisher = {arXiv},
  doi = {10.48550/arXiv.2212.07292},
  urldate = {2022-12-21},
  abstract = {Unsupervised sim-to-real domain adaptation (UDA) for semantic segmentation aims to improve the real-world test performance of a model trained on simulated data. It can save the cost of manually labeling data in real-world applications such as robot vision and autonomous driving. Traditional UDA often assumes that there are abundant unlabeled real-world data samples available during training for the adaptation. However, such an assumption does not always hold in practice owing to the collection difficulty and the scarcity of the data. Thus, we aim to relieve this need on a large number of real data, and explore the one-shot unsupervised sim-to-real domain adaptation (OSUDA) and generalization (OSDG) problem, where only one real-world data sample is available. To remedy the limited real data knowledge, we first construct the pseudo-target domain by stylizing the simulated data with the one-shot real data. To mitigate the sim-to-real domain gap on both the style and spatial structure level and facilitate the sim-to-real adaptation, we further propose to use class-aware cross-domain transformers with an intermediate domain randomization strategy to extract the domain-invariant knowledge, from both the simulated and pseudo-target data. We demonstrate the effectiveness of our approach for OSUDA and OSDG on different benchmarks, outperforming the state-of-the-art methods by a large margin, 10.87, 9.59, 13.05 and 15.91 mIoU on GTA, SYNTHIA\${\textbackslash}rightarrow\$Cityscapes, Foggy Cityscapes, respectively.},
  archiveprefix = {arXiv},
  keywords = {Computer Science - Computer Vision and Pattern Recognition}
}

@inproceedings{hermann2020whata,
  title = {What Shapes Feature Representations? {{Exploring}} Datasets, Architectures, and Training},
  shorttitle = {What Shapes Feature Representations?},
  booktitle = {Advances in {{Neural Information Processing Systems}}},
  author = {Hermann, Katherine and Lampinen, Andrew},
  year = {2020},
  volume = {33},
  pages = {9995--10006},
  publisher = {Curran Associates, Inc.},
  urldate = {2022-10-18},
  abstract = {In naturalistic learning problems, a model's input contains a wide range of features, some useful for the task at hand, and others not. Of the useful features, which ones does the model use? Of the task-irrelevant features, which ones does the model represent? Answers to these questions are important for understanding the basis of models' decisions, as well as for building models that learn versatile, adaptable representations useful beyond the original training task. We study these questions using synthetic datasets in which the task-relevance of input features can be controlled directly. We find that when two features redundantly predict the labels, the model preferentially represents one, and its preference reflects what was most linearly decodable from the untrained model. Over training, task-relevant features are enhanced, and task-irrelevant features are partially suppressed. Interestingly, in some cases, an easier, weakly predictive feature can suppress a more strongly predictive, but more difficult one. Additionally, models trained to recognize both easy and hard features learn representations most similar to models that use only the easy feature. Further, easy features lead to more consistent representations across model runs than do hard features. Finally, models have greater representational similarity to an untrained model than to models trained on a different task. Our results highlight the complex processes that determine which features a model represents.},
  file = {C:\Users\steph\Zotero\storage\6NGNZCNY\Hermann and Lampinen - 2020 - What shapes feature representations Exploring dat.pdf}
}

@misc{hess2024knowledge,
  title = {Knowledge {{Accumulation}} in {{Continually Learned Representations}} and the {{Issue}} of {{Feature Forgetting}}},
  author = {Hess, Timm and Verwimp, Eli and van de Ven, Gido M. and Tuytelaars, Tinne},
  year = {2024},
  month = jun,
  number = {arXiv:2304.00933},
  eprint = {2304.00933},
  primaryclass = {cs},
  publisher = {arXiv},
  doi = {10.48550/arXiv.2304.00933},
  urldate = {2025-03-09},
  abstract = {Continual learning research has shown that neural networks suffer from catastrophic forgetting "at the output level", but it is debated whether this is also the case at the level of learned representations. Multiple recent studies ascribe representations a certain level of innate robustness against forgetting -- that they only forget minimally in comparison with forgetting at the output level. We revisit and expand upon the experiments that revealed this difference in forgetting and illustrate the coexistence of two phenomena that affect the quality of continually learned representations: knowledge accumulation and feature forgetting. Taking both aspects into account, we show that, even though forgetting in the representation (i.e. feature forgetting) can be small in absolute terms, when measuring relative to how much was learned during a task, forgetting in the representation tends to be just as catastrophic as forgetting at the output level. Next we show that this feature forgetting is problematic as it substantially slows down the incremental learning of good general representations (i.e. knowledge accumulation). Finally, we study how feature forgetting and knowledge accumulation are affected by different types of continual learning methods.},
  archiveprefix = {arXiv},
  keywords = {Computer Science - Computer Vision and Pattern Recognition,Computer Science - Machine Learning},
  file = {C\:\\Users\\steph\\Zotero\\storage\\9QC7CJWT\\Hess et al. - 2024 - Knowledge Accumulation in Continually Learned Representations and the Issue of Feature Forgetting.pdf;C\:\\Users\\steph\\Zotero\\storage\\NBTR4RB7\\2304.html}
}

@article{huang2024deeplearningbased,
  title = {Deep-{{Learning-Based Semantic Segmentation}} of {{Remote Sensing Images}}: {{A Survey}}},
  shorttitle = {Deep-{{Learning-Based Semantic Segmentation}} of {{Remote Sensing Images}}},
  author = {Huang, Liwei and Jiang, Bitao and Lv, Shouye and Liu, Yanbo and Fu, Ying},
  year = {2024},
  journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
  volume = {17},
  pages = {8370--8396},
  issn = {2151-1535},
  doi = {10.1109/JSTARS.2023.3335891},
  urldate = {2024-07-24},
  abstract = {Semantic segmentation of remote sensing images (SSRSIs), which aims to assign a category to each pixel in remote sensing images, plays a vital role in a broad range of applications, such as environmental monitoring, urban planning, and land resource utilization. Recently, with the successful application of deep learning in remote sensing, a substantial amount of work has been aimed at developing SSRSI methods using deep learning models. In this survey, we provide a comprehensive review of SSRSI. First, we review the current mainstream semantic segmentation models based on deep learning. Next, we analyze the main challenges faced by SSRSI and comprehensively summarize the current research status of deep-learning-based SSRSI, especially some new directions in SSRSI are outlined, including semisupervised and weakly-supervised SSRSI, unsupervised domain adaption in SSRSI, multimodal data-fusion-based SSRSI, and pretrained models for SSRSI. Then, we examine the most widely used datasets and metrics and review the quantitative results and experimental performance of some representative methods of SSRSI. Finally, we discuss promising future research directions in this area.},
  keywords = {Adaptation models,Computational modeling,Context modeling,Deep learning,multimodal fusion,pretrained models,Remote sensing,remote sensing images,semantic segmentation,Semantic segmentation,semisupervised,Task analysis,Transformers,unsupervised domain adaptation,weakly-supervised},
  file = {C\:\\Users\\steph\\Zotero\\storage\\HEHULDZW\\Huang et al. - 2024 - Deep-Learning-Based Semantic Segmentation of Remot.pdf;C\:\\Users\\steph\\Zotero\\storage\\V6N5GPPY\\10330561.html}
}

@inproceedings{huang2025classincremental,
  title = {Class-{{Incremental Learning}} with~{{CLIP}}: {{Adaptive Representation Adjustment}} and~{{Parameter Fusion}}},
  shorttitle = {Class-{{Incremental Learning}} with~{{CLIP}}},
  booktitle = {Computer {{Vision}} -- {{ECCV}} 2024},
  author = {Huang, Linlan and Cao, Xusheng and Lu, Haori and Liu, Xialei},
  editor = {Leonardis, Ale{\v s} and Ricci, Elisa and Roth, Stefan and Russakovsky, Olga and Sattler, Torsten and Varol, G{\"u}l},
  year = {2025},
  pages = {214--231},
  publisher = {Springer Nature Switzerland},
  address = {Cham},
  doi = {10.1007/978-3-031-72949-2_13},
  abstract = {Class-incremental learning is a challenging problem, where the goal is to train a model that can classify data from an increasing number of classes over time. With the advancement of vision-language pre-trained models such as CLIP, they demonstrate good generalization ability that allows them to excel in class-incremental learning with completely frozen parameters. However, further adaptation to downstream tasks by simply fine-tuning the model leads to severe forgetting. Most existing works with pre-trained models assume that the forgetting of old classes is uniform when the model acquires new knowledge. In this paper, we propose a method named Adaptive Representation Adjustment and Parameter Fusion (RAPF). During training for new data, we measure the influence of new classes on old ones and adjust the representations, using textual features. After training, we employ a decomposed parameter fusion to further mitigate forgetting during adapter module fine-tuning. Experiments on several conventional benchmarks show that our method achieves state-of-the-art results. Our code is available at https://github.com/linlany/RAPF.},
  isbn = {978-3-031-72949-2},
  langid = {english}
}

@misc{immanuel2024learnable,
  title = {Learnable {{Prompt}} for {{Few-Shot Semantic Segmentation}} in {{Remote Sensing Domain}}},
  author = {Immanuel, Steve Andreas and Sinulingga, Hagai Raja},
  year = {2024},
  month = apr,
  number = {arXiv:2404.10307},
  eprint = {2404.10307},
  primaryclass = {cs},
  publisher = {arXiv},
  doi = {10.48550/arXiv.2404.10307},
  urldate = {2024-09-18},
  abstract = {Few-shot segmentation is a task to segment objects or regions of novel classes within an image given only a few annotated examples. In the generalized setting, the task extends to segment both the base and the novel classes. The main challenge is how to train the model such that the addition of novel classes does not hurt the base classes performance, also known as catastrophic forgetting. To mitigate this issue, we use SegGPT as our base model and train it on the base classes. Then, we use separate learnable prompts to handle predictions for each novel class. To handle various object sizes which typically present in remote sensing domain, we perform patch-based prediction. To address the discontinuities along patch boundaries, we propose a patch-and-stitch technique by re-framing the problem as an image inpainting task. During inference, we also utilize image similarity search over image embeddings for prompt selection and novel class filtering to reduce false positive predictions. Based on our experiments, our proposed method boosts the weighted mIoU of a simple fine-tuned SegGPT from 15.96 to 35.08 on the validation set of few-shot OpenEarthMap dataset given in the challenge.},
  archiveprefix = {arXiv},
  keywords = {Computer Science - Artificial Intelligence,Computer Science - Computer Vision and Pattern Recognition},
  file = {C\:\\Users\\steph\\Zotero\\storage\\7DKV7NNS\\Immanuel et Sinulingga - 2024 - Learnable Prompt for Few-Shot Semantic Segmentatio.pdf;C\:\\Users\\steph\\Zotero\\storage\\M9C4DPIQ\\2404.html}
}

@misc{islam2021shape,
  title = {Shape or {{Texture}}: {{Understanding Discriminative Features}} in {{CNNs}}},
  shorttitle = {Shape or {{Texture}}},
  author = {Islam, Md Amirul and Kowal, Matthew and Esser, Patrick and Jia, Sen and Ommer, Bjorn and Derpanis, Konstantinos G. and Bruce, Neil},
  year = {2021},
  month = jan,
  number = {arXiv:2101.11604},
  eprint = {2101.11604},
  primaryclass = {cs},
  publisher = {arXiv},
  doi = {10.48550/arXiv.2101.11604},
  urldate = {2022-10-18},
  abstract = {Contrasting the previous evidence that neurons in the later layers of a Convolutional Neural Network (CNN) respond to complex object shapes, recent studies have shown that CNNs actually exhibit a `texture bias': given an image with both texture and shape cues (e.g., a stylized image), a CNN is biased towards predicting the category corresponding to the texture. However, these previous studies conduct experiments on the final classification output of the network, and fail to robustly evaluate the bias contained (i) in the latent representations, and (ii) on a per-pixel level. In this paper, we design a series of experiments that overcome these issues. We do this with the goal of better understanding what type of shape information contained in the network is discriminative, where shape information is encoded, as well as when the network learns about object shape during training. We show that a network learns the majority of overall shape information at the first few epochs of training and that this information is largely encoded in the last few layers of a CNN. Finally, we show that the encoding of shape does not imply the encoding of localized per-pixel semantic information. The experimental results and findings provide a more accurate understanding of the behaviour of current CNNs, thus helping to inform future design choices.},
  archiveprefix = {arXiv},
  keywords = {Computer Science - Computer Vision and Pattern Recognition},
  file = {C\:\\Users\\steph\\Zotero\\storage\\ENG9UGPZ\\Islam et al. - 2021 - Shape or Texture Understanding Discriminative Fea.pdf;C\:\\Users\\steph\\Zotero\\storage\\8CYLEECS\\2101.html}
}

@article{jain2021semask,
  title = {{{SeMask}}: {{Semantically Masked Transformers}} for {{Semantic Segmentation}}},
  shorttitle = {{{SeMask}}},
  author = {Jain, Jitesh and Singh, Anukriti and Orlov, Nikita and Huang, Zilong and Li, Jiachen and Walton, Steven and Shi, Humphrey},
  year = {2021},
  month = dec,
  journal = {arXiv:2112.12782 [cs]},
  eprint = {2112.12782},
  primaryclass = {cs},
  urldate = {2022-01-21},
  abstract = {Finetuning a pretrained backbone in the encoder part of an image transformer network has been the traditional approach for the semantic segmentation task. However, such an approach leaves out the semantic context that an image provides during the encoding stage. This paper argues that incorporating semantic information of the image into pretrained hierarchical transformer-based backbones while finetuning improves the performance considerably. To achieve this, we propose SeMask, a simple and effective framework that incorporates semantic information into the encoder with the help of a semantic attention operation. In addition, we use a lightweight semantic decoder during training to provide supervision to the intermediate semantic prior maps at every stage. Our experiments demonstrate that incorporating semantic priors enhances the performance of the established hierarchical encoders with a slight increase in the number of FLOPs. We provide empirical proof by integrating SeMask into each variant of the Swin-Transformer as our encoder paired with different decoders. Our framework achieves a new state-of-the-art of 58.22\% mIoU on the ADE20K dataset and improvements of over 3\% in the mIoU metric on the Cityscapes dataset. The code and checkpoints are publicly available at https://github.com/Picsart-AI-Research/SeMask-Segmentation .},
  archiveprefix = {arXiv},
  keywords = {attention,Computer Science - Computer Vision and Pattern Recognition,Computer Science - Machine Learning,transformer},
  file = {C\:\\Users\\steph\\Zotero\\storage\\HM37L2CJ\\Jain et al. - 2021 - SeMask Semantically Masked Transformers for Seman.pdf;C\:\\Users\\steph\\Zotero\\storage\\QMIPITJC\\Jain et al. - 2021 - SeMask Semantically Masked Transformers for Seman.pdf;C\:\\Users\\steph\\Zotero\\storage\\YTZHYYBX\\2112.html}
}

@inproceedings{jain2023oneformer,
  title = {{{OneFormer}}: {{One Transformer To Rule Universal Image Segmentation}}},
  shorttitle = {{{OneFormer}}},
  booktitle = {Proceedings of the {{IEEE}}/{{CVF Conference}} on {{Computer Vision}} and {{Pattern Recognition}}},
  author = {Jain, Jitesh and Li, Jiachen and Chiu, Mang Tik and Hassani, Ali and Orlov, Nikita and Shi, Humphrey},
  year = {2023},
  pages = {2989--2998},
  urldate = {2023-06-16},
  langid = {english},
  file = {C:\Users\steph\Zotero\storage\9B5FZ734\Jain et al. - 2023 - OneFormer One Transformer To Rule Universal Image.pdf}
}

@inproceedings{jain2023oneformera,
  title = {{{OneFormer}}: {{One Transformer To Rule Universal Image Segmentation}}},
  shorttitle = {{{OneFormer}}},
  booktitle = {Proceedings of the {{IEEE}}/{{CVF Conference}} on {{Computer Vision}} and {{Pattern Recognition}}},
  author = {Jain, Jitesh and Li, Jiachen and Chiu, Mang Tik and Hassani, Ali and Orlov, Nikita and Shi, Humphrey},
  year = {2023},
  pages = {2989--2998},
  urldate = {2024-07-22},
  langid = {english},
  file = {C:\Users\steph\Zotero\storage\X7HRR67F\Jain et al. - 2023 - OneFormer One Transformer To Rule Universal Image.pdf}
}

@misc{janson2023simple,
  title = {A {{Simple Baseline}} That {{Questions}} the {{Use}} of {{Pretrained-Models}} in {{Continual Learning}}},
  author = {Janson, Paul and Zhang, Wenxuan and Aljundi, Rahaf and Elhoseiny, Mohamed},
  year = {2023},
  month = mar,
  number = {arXiv:2210.04428},
  eprint = {2210.04428},
  primaryclass = {cs},
  publisher = {arXiv},
  doi = {10.48550/arXiv.2210.04428},
  urldate = {2024-04-15},
  abstract = {With the success of pretraining techniques in representation learning, a number of continual learning methods based on pretrained models have been proposed. Some of these methods design continual learning mechanisms on the pre-trained representations and only allow minimum updates or even no updates of the backbone models during the training of continual learning. In this paper, we question whether the complexity of these models is needed to achieve good performance by comparing them to a simple baseline that we designed. We argue that the pretrained feature extractor itself can be strong enough to achieve a competitive or even better continual learning performance on Split-CIFAR100 and CoRe 50 benchmarks. To validate this, we conduct a very simple baseline that 1) use the frozen pretrained model to extract image features for every class encountered during the continual learning stage and compute their corresponding mean features on training data, and 2) predict the class of the input based on the nearest neighbor distance between test samples and mean features of the classes; i.e., Nearest Mean Classifier (NMC). This baseline is single-headed, exemplar-free, and can be task-free (by updating the means continually). This baseline achieved 88.53\% on 10-Split-CIFAR-100, surpassing most state-of-the-art continual learning methods that are all initialized using the same pretrained transformer model. We hope our baseline may encourage future progress in designing learning systems that can continually add quality to the learning representations even if they started from some pretrained weights.},
  archiveprefix = {arXiv},
  keywords = {Computer Science - Computer Vision and Pattern Recognition,Computer Science - Machine Learning},
  file = {C\:\\Users\\steph\\Zotero\\storage\\RQZYVXU6\\Janson et al. - 2023 - A Simple Baseline that Questions the Use of Pretra.pdf;C\:\\Users\\steph\\Zotero\\storage\\PD96G23F\\2210.html}
}

@article{jiang2022fewshot,
  title = {Few-{{Shot Segmentation}} of {{Remote Sensing Images Using Deep Metric Learning}}},
  author = {Jiang, Xufeng and Zhou, Nan and Li, Xiang},
  year = {2022},
  journal = {IEEE Geoscience and Remote Sensing Letters},
  volume = {19},
  pages = {1--5},
  issn = {1558-0571},
  doi = {10.1109/LGRS.2022.3154402},
  urldate = {2024-09-20},
  abstract = {Current convolutional neural network (CNN)-based methods for remote sensing image segmentation require a large number of densely annotated images for model training and have limited generalization abilities for unseen object categories. In this letter, we propose a novel few-shot learning-based method for the semantic segmentation of remote sensing images. Our method can perform semantic labeling for unseen object categories with only a few annotated samples. More specifically, our model starts by using a deep CNN to extract high-level semantic features. The prototype representation of each class is then generated by using a masked average pooling on the feature embeddings of the support images with ground truth masks. Finally, our model performs semantic labeling over the query images by matching the feature embedding of each pixel to its nearest prototypes in the embedding space. Our model is optimized with a nonparametric metric learning-based loss function to maximize the intra-class similarity of learned prototypes while minimizing the inter-class similarity. Experiments on International Society for Photogrammetry and Remote Sensing (ISPRS) 2-D semantic labeling dataset demonstrate satisfying in-domain and cross-domain transferring abilities of our model.},
  keywords = {Feature extraction,Few-shot learning,Image segmentation,Labeling,prototype representation,Prototypes,remote sensing,Remote sensing,semantic segmentation,Semantics,Training},
  file = {C\:\\Users\\steph\\Zotero\\storage\\NC5JUUGT\\Jiang et al. - 2022 - Few-Shot Segmentation of Remote Sensing Images Usi.pdf;C\:\\Users\\steph\\Zotero\\storage\\6RSWLUGX\\9721235.html}
}

@article{jiao2023braininspired,
  title = {Brain-{{Inspired Remote Sensing Foundation Models}} and {{Open Problems}}: {{A Comprehensive Survey}}},
  shorttitle = {Brain-{{Inspired Remote Sensing Foundation Models}} and {{Open Problems}}},
  author = {Jiao, Licheng and Huang, Zhongjian and Lu, Xiaoqiang and Liu, Xu and Yang, Yuting and Zhao, Jiaxuan and Zhang, Jinyue and Hou, Biao and Yang, Shuyuan and Liu, Fang and Ma, Wenping and Li, Lingling and Zhang, Xiangrong and Chen, Puhua and Feng, Zhixi and Tang, Xu and Guo, Yuwei and Quan, Dou and Wang, Shuang and Li, Weibin and Bai, Jing and Li, Yangyang and Shang, Ronghua and Feng, Jie},
  year = {2023},
  journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
  volume = {16},
  pages = {10084--10120},
  issn = {2151-1535},
  doi = {10.1109/JSTARS.2023.3316302},
  urldate = {2024-07-24},
  abstract = {The foundation model (FM) has garnered significant attention for its remarkable transfer performance in downstream tasks. Typically, it undergoes task-agnostic pretraining on a large dataset and can be efficiently adapted to various downstream applications through fine-tuning. While FMs have been extensively explored in language and other domains, their potential in remote sensing has also begun to attract scholarly interest. However, comprehensive investigations and performance comparisons of these models on remote sensing tasks are currently lacking. In this survey, we provide essential background knowledge by introducing key technologies and recent developments in FMs. Subsequently, we explore essential downstream applications in remote sensing, covering classification, localization, and understanding. Our analysis encompasses over 30 FMs in both natural and remote sensing fields, and we conduct extensive experiments on more than 10 datasets, evaluating global feature representation, local feature representation, and target localization. Through quantitative assessments, we highlight the distinctions among various FMs and confirm that pretrained large-scale natural FMs can also deliver outstanding performance in remote sensing tasks. After that, we systematically presented a brain-inspired framework for remote sensing foundation models (RSFMs). We delve into the brain-inspired characteristics in this framework, including structure, perception, learning, and cognition. To conclude, we summarize 12 open problems in RSFMs, providing potential research directions. Our survey offers valuable insights into the burgeoning field of RSFMs and aims to foster further advancements in this exciting area.},
  keywords = {Adaptation models,Brain modeling,Data models,deep learning,foundation model,Frequency modulation,image analysis,remote sensing,Remote sensing,Task analysis,Transformers},
  file = {C\:\\Users\\steph\\Zotero\\storage\\YUEGJ94A\\Jiao et al. - 2023 - Brain-Inspired Remote Sensing Foundation Models an.pdf;C\:\\Users\\steph\\Zotero\\storage\\VWSH44BL\\10254282.html}
}

@inproceedings{jitkrittum2018informative,
  title = {Informative {{Features}} for {{Model Comparison}}},
  booktitle = {Advances in {{Neural Information Processing Systems}}},
  author = {Jitkrittum, Wittawat and Kanagawa, Heishiro and Sangkloy, Patsorn and Hays, James and Sch{\"o}lkopf, Bernhard and Gretton, Arthur},
  year = {2018},
  volume = {31},
  publisher = {Curran Associates, Inc.},
  urldate = {2022-12-20},
  abstract = {Given two candidate models, and a set of target observations, we address the problem of measuring the relative goodness of fit of the two models. We propose two new statistical tests which are nonparametric, computationally efficient (runtime complexity is linear in the sample size), and interpretable. As a unique advantage, our tests can produce a set of examples (informative features) indicating the regions in the data domain where one model fits significantly better than the other. In a real-world problem of comparing GAN models, the test power of our new test matches that of the state-of-the-art test of relative goodness of fit, while being one order of magnitude faster.},
  file = {C:\Users\steph\Zotero\storage\C5CVDH7Z\Jitkrittum et al. - 2018 - Informative Features for Model Comparison.pdf}
}

@inproceedings{kang2023distilling,
  title = {Distilling {{Self-Supervised Vision Transformers}} for {{Weakly-Supervised Few-Shot Classification}} \& {{Segmentation}}},
  booktitle = {Proceedings of the {{IEEE}}/{{CVF Conference}} on {{Computer Vision}} and {{Pattern Recognition}}},
  author = {Kang, Dahyun and Koniusz, Piotr and Cho, Minsu and Murray, Naila},
  year = {2023},
  pages = {19627--19638},
  urldate = {2023-09-18},
  langid = {english},
  file = {C:\Users\steph\Zotero\storage\2QQCGGIW\Kang et al. - 2023 - Distilling Self-Supervised Vision Transformers for.pdf}
}

@misc{kumar2022finetuning,
  title = {Fine-{{Tuning}} Can {{Distort Pretrained Features}} and {{Underperform Out-of-Distribution}}},
  author = {Kumar, Ananya and Raghunathan, Aditi and Jones, Robbie and Ma, Tengyu and Liang, Percy},
  year = {2022},
  month = feb,
  journal = {arXiv.org},
  urldate = {2025-03-09},
  abstract = {When transferring a pretrained model to a downstream task, two popular methods are full fine-tuning (updating all the model parameters) and linear probing (updating only the last linear layer -- the "head"). It is well known that fine-tuning leads to better accuracy in-distribution (ID). However, in this paper, we find that fine-tuning can achieve worse accuracy than linear probing out-of-distribution (OOD) when the pretrained features are good and the distribution shift is large. On 10 distribution shift datasets (Breeds-Living17, Breeds-Entity30, DomainNet, CIFAR \${\textbackslash}to\$ STL, CIFAR10.1, FMoW, ImageNetV2, ImageNet-R, ImageNet-A, ImageNet-Sketch), fine-tuning obtains on average 2\% higher accuracy ID but 7\% lower accuracy OOD than linear probing. We show theoretically that this tradeoff between ID and OOD accuracy arises even in a simple setting: fine-tuning overparameterized two-layer linear networks. We prove that the OOD error of fine-tuning is high when we initialize with a fixed or random head -- this is because while fine-tuning learns the head, the lower layers of the neural network change simultaneously and distort the pretrained features. Our analysis suggests that the easy two-step strategy of linear probing then full fine-tuning (LP-FT), sometimes used as a fine-tuning heuristic, combines the benefits of both fine-tuning and linear probing. Empirically, LP-FT outperforms both fine-tuning and linear probing on the above datasets (1\% better ID, 10\% better OOD than full fine-tuning).},
  howpublished = {https://arxiv.org/abs/2202.10054v1},
  langid = {english},
  file = {C:\Users\steph\Zotero\storage\CZ94XKEQ\Kumar et al. - 2022 - Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution.pdf}
}

@misc{lange2023continual,
  title = {Continual Evaluation for Lifelong Learning: {{Identifying}} the Stability Gap},
  shorttitle = {Continual Evaluation for Lifelong Learning},
  author = {Lange, Matthias De and van de Ven, Gido and Tuytelaars, Tinne},
  year = {2023},
  month = mar,
  number = {arXiv:2205.13452},
  eprint = {2205.13452},
  primaryclass = {cs},
  publisher = {arXiv},
  doi = {10.48550/arXiv.2205.13452},
  urldate = {2025-02-23},
  abstract = {Time-dependent data-generating distributions have proven to be difficult for gradient-based training of neural networks, as the greedy updates result in catastrophic forgetting of previously learned knowledge. Despite the progress in the field of continual learning to overcome this forgetting, we show that a set of common state-of-the-art methods still suffers from substantial forgetting upon starting to learn new tasks, except that this forgetting is temporary and followed by a phase of performance recovery. We refer to this intriguing but potentially problematic phenomenon as the stability gap. The stability gap had likely remained under the radar due to standard practice in the field of evaluating continual learning models only after each task. Instead, we establish a framework for continual evaluation that uses per-iteration evaluation and we define a new set of metrics to quantify worst-case performance. Empirically we show that experience replay, constraint-based replay, knowledge-distillation, and parameter regularization methods are all prone to the stability gap; and that the stability gap can be observed in class-, task-, and domain-incremental learning benchmarks. Additionally, a controlled experiment shows that the stability gap increases when tasks are more dissimilar. Finally, by disentangling gradients into plasticity and stability components, we propose a conceptual explanation for the stability gap.},
  archiveprefix = {arXiv},
  keywords = {Computer Science - Artificial Intelligence,Computer Science - Computer Vision and Pattern Recognition,Computer Science - Machine Learning},
  file = {C\:\\Users\\steph\\Zotero\\storage\\UQK8BS4K\\Lange et al. - 2023 - Continual evaluation for lifelong learning Identifying the stability gap.pdf;C\:\\Users\\steph\\Zotero\\storage\\FQYFUMKD\\2205.html}
}

@inproceedings{lee2023pretrained,
  title = {Do {{Pre-Trained Models Benefit Equally}} in {{Continual Learning}}?},
  booktitle = {Proceedings of the {{IEEE}}/{{CVF Winter Conference}} on {{Applications}} of {{Computer Vision}}},
  author = {Lee, Kuan-Ying and Zhong, Yuanyi and Wang, Yu-Xiong},
  year = {2023},
  pages = {6485--6493},
  urldate = {2025-03-09},
  langid = {english},
  file = {C:\Users\steph\Zotero\storage\XWKNBIGI\Lee et al. - 2023 - Do Pre-Trained Models Benefit Equally in Continual Learning.pdf}
}

@inproceedings{leeb2022exploring,
  title = {Exploring the {{Latent Space}} of {{Autoencoders}} with {{Interventional Assays}}},
  booktitle = {Advances in {{Neural Information Processing Systems}}},
  author = {Leeb, Felix and Bauer, Stefan and Besserve, Michel and Sch{\"o}lkopf, Bernhard},
  year = {2022},
  month = oct,
  urldate = {2022-12-05},
  abstract = {Autoencoders exhibit impressive abilities to embed the data manifold into a low-dimensional latent space, making them a staple of representation learning methods. However, without explicit supervision, which is often unavailable, the representation is usually uninterpretable, making analysis and principled progress challenging. We propose a framework, called latent responses, which exploits the locally contractive behavior exhibited by variational autoencoders to explore the learned manifold. More specifically, we develop tools to probe the representation using interventions in the latent space to quantify the relationships between latent variables. We extend the notion of disentanglement to take the learned generative process into account and consequently avoid the limitations of existing metrics that may rely on spurious correlations. Our analyses underscore the importance of studying the causal structure of the representation to improve performance on downstream tasks such as generation, interpolation, and inference of the factors of variation.},
  langid = {english},
  file = {C\:\\Users\\steph\\Zotero\\storage\\HATFE4M7\\Leeb et al. - 2022 - Exploring the Latent Space of Autoencoders with In.pdf;C\:\\Users\\steph\\Zotero\\storage\\6EUC6R26\\forum.html}
}

@article{li2011hyperspectral,
  title = {Hyperspectral Image Segmentation Using a New {{Bayesian}} Approach with Active Learning},
  author = {Li, Jun and {Bioucas-Dias}, Jos{\'e} M and Plaza, Antonio},
  year = {2011},
  journal = {IEEE Transactions on Geoscience and Remote Sensing},
  volume = {49},
  number = {10},
  pages = {3947--3960},
  publisher = {IEEE}
}

@article{li2021panoptic,
  title = {Panoptic {{SegFormer}}: {{Delving Deeper}} into {{Panoptic Segmentation}} with {{Transformers}}},
  shorttitle = {Panoptic {{SegFormer}}},
  author = {Li, Zhiqi and Wang, Wenhai and Xie, Enze and Yu, Zhiding and Anandkumar, Anima and Alvarez, Jose M. and Lu, Tong and Luo, Ping},
  year = {2021},
  month = dec,
  journal = {arXiv:2109.03814 [cs]},
  eprint = {2109.03814},
  primaryclass = {cs},
  urldate = {2022-01-21},
  abstract = {Panoptic segmentation involves a combination of joint semantic segmentation and instance segmentation, where image contents are divided into two types: things and stuff. We present Panoptic SegFormer, a general framework for panoptic segmentation with transformers. It contains three innovative components: an efficient deeply-supervised mask decoder, a query decoupling strategy, and an improved post-processing method. We also use Deformable DETR to efficiently process multi-scale features, which is a fast and efficient version of DETR. Specifically, we supervise the attention modules in the mask decoder in a layer-wise manner. This deep supervision strategy lets the attention modules quickly focus on meaningful semantic regions. It improves performance and reduces the number of required training epochs by half compared to Deformable DETR. Our query decoupling strategy decouples the responsibilities of the query set and avoids mutual interference between things and stuff. In addition, our post-processing strategy improves performance without additional costs by jointly considering classification and segmentation qualities to resolve conflicting mask overlaps. Our approach increases the accuracy 6.2{\textbackslash}\% PQ over the baseline DETR model. Panoptic SegFormer achieves state-of-the-art results on COCO test-dev with 56.2{\textbackslash}\% PQ. It also shows stronger zero-shot robustness over existing methods. The code is released at {\textbackslash}url\{https://github.com/zhiqi-li/Panoptic-SegFormer\}.},
  archiveprefix = {arXiv},
  keywords = {Computer Science - Computer Vision and Pattern Recognition,segmentation,transformer},
  file = {C\:\\Users\\steph\\Zotero\\storage\\FQS396QV\\Li et al. - 2021 - Panoptic SegFormer Delving Deeper into Panoptic S.pdf;C\:\\Users\\steph\\Zotero\\storage\\FQ3XKCPE\\2109.html}
}

@article{li2022classincremental,
  title = {Class-{{Incremental Learning Network}} for {{Small Objects Enhancing}} of {{Semantic Segmentation}} in {{Aerial Imagery}}},
  author = {Li, Junxi and Sun, Xian and Diao, Wenhui and Wang, Peijin and Feng, Yingchao and Lu, Xiaonan and Xu, Guangluan},
  year = {2022},
  journal = {IEEE Transactions on Geoscience and Remote Sensing},
  volume = {60},
  pages = {1--20},
  issn = {1558-0644},
  doi = {10.1109/TGRS.2021.3124303},
  urldate = {2024-06-19},
  abstract = {Due to the differences in the feature distribution between classes, when the model learns in a continuous data stream, it will encounter catastrophic forgetting. The incremental learning methods have shown great potential to solve this problem. However, most existing methods based on task-incremental learning are difficult to adapt to characteristics of remote sensing scenes with few differences in appearance but large differences in features, which is not conducive to artificially distinguish task-identity document (ID). Thus, we propose a class-incremental learning (CIL) network for small objects enhancing semantic segmentation in aerial imagery. Specifically, considering the superior accuracy of the binary classifier, we propose a twin-auxiliary (TA) model that adds an auxiliary binary classification task. Then, for expansion and contraction at the edge and small object confusion problems, we introduce a diversity distillation loss, using the results of binary-classifier to constrain the multiclass segmentation results and strengthen the attention to the locations of the segmentation results that have changed. Finally, we design a conflict reduction mechanism for multihead classifier to achieve single-head prediction for CIL. Experiments demonstrate that our method has good performance on the Vaihingen and Potsdam datasets by the International Society for Photogrammetry and Remote Sensing (ISPRS), outperforming state-of-the-art (SOTA) incremental learning methods. The code will be available soon.},
  keywords = {Catastrophic forgetting,Data models,edge contraction,Image segmentation,incremental learning,knowledge distillation,Learning systems,Pipelines,Remote sensing,Semantics,small-target confusion,Task analysis},
  file = {C:\Users\steph\Zotero\storage\5FFVCMFM\Li et al. - 2022 - Class-Incremental Learning Network for Small Objec.pdf}
}

@article{li2022silland,
  title = {{{SIL-LAND}}: {{Segmentation Incremental Learning}} in {{Aerial Imagery}} via {{LAbel Number Distribution Consistency}}},
  shorttitle = {{{SIL-LAND}}},
  author = {Li, Junxi and Diao, Wenhui and Lu, Xiaonan and Wang, Peijin and Zhang, Yidan and Yang, Zhujun and Xu, Guangluan and Sun, Xian},
  year = {2022},
  journal = {IEEE Transactions on Geoscience and Remote Sensing},
  volume = {60},
  pages = {1--20},
  issn = {1558-0644},
  doi = {10.1109/TGRS.2022.3199584},
  urldate = {2024-06-19},
  abstract = {Segmentation incremental learning (SIL) has received a lot of attention in recent years due to the ability to overcome the problem of catastrophic forgetting. Our study found that differences in label number distribution (LAND) affect the performance of SIL. Because the labels for pixels of the old category are marked as background when the model is trained on the new tasks, the LAND is inconsistent with static learning that is considered to be the upper bound on incremental learning, which hinders the mitigation of the catastrophic forgetting problem. In response to the above problems, we propose an incremental learning method named SIL-LAND, which improves the accuracy by making the LAND of our method close to that of static learning. From the perspective of high-level semantic labels, we propose the prototype update mechanism for the problem that nonadaptive representative prototypes ignore the sample diversity of semantic categories in remote sensing images. By compensating for the difference in LAND at the feature level, the distance between the prototype and the actual class center is reduced; aiming at the lack of semantic consistency between feature vectors and prototypes, we propose a similarity measure module to increase the intraclass similarity between the prototype and the corresponding feature vectors. From the perspective of one-hot labels, we propose label reconstruction, including foreground screening and background padding to make the number distribution of one-hot labels as close as possible to that of static learning. A series of experimental results demonstrates the effectiveness of our method.},
  keywords = {Computational modeling,Data models,Incremental learning,label number distribution (LAND),label reconstruction (LR),prototype update,Prototypes,Remote sensing,Semantics,similarity measure,Task analysis,Training},
  file = {C:\Users\steph\Zotero\storage\RJ94FHVQ\Li et al. - 2022 - SIL-LAND Segmentation Incremental Learning in Aer.pdf}
}

@inproceedings{li2023mask,
  title = {Mask {{DINO}}: {{Towards}} a {{Unified Transformer-Based Framework}} for {{Object Detection}} and {{Segmentation}}},
  shorttitle = {Mask {{DINO}}},
  booktitle = {Proceedings of the {{IEEE}}/{{CVF Conference}} on {{Computer Vision}} and {{Pattern Recognition}}},
  author = {Li, Feng and Zhang, Hao and Xu, Huaizhe and Liu, Shilong and Zhang, Lei and Ni, Lionel M. and Shum, Heung-Yeung},
  year = {2023},
  pages = {3041--3050},
  urldate = {2024-07-22},
  langid = {english},
  file = {C:\Users\steph\Zotero\storage\EI89ZK7I\Li et al. - 2023 - Mask DINO Towards a Unified Transformer-Based Fra.pdf}
}

@misc{li2023transformerbased,
  title = {Transformer-{{Based Visual Segmentation}}: {{A Survey}}},
  shorttitle = {Transformer-{{Based Visual Segmentation}}},
  author = {Li, Xiangtai and Ding, Henghui and Yuan, Haobo and Zhang, Wenwei and Pang, Jiangmiao and Cheng, Guangliang and Chen, Kai and Liu, Ziwei and Loy, Chen Change},
  year = {2023},
  month = dec,
  number = {arXiv:2304.09854},
  eprint = {2304.09854},
  primaryclass = {cs},
  publisher = {arXiv},
  doi = {10.48550/arXiv.2304.09854},
  urldate = {2024-02-28},
  abstract = {Visual segmentation seeks to partition images, video frames, or point clouds into multiple segments or groups. This technique has numerous real-world applications, such as autonomous driving, image editing, robot sensing, and medical analysis. Over the past decade, deep learning-based methods have made remarkable strides in this area. Recently, transformers, a type of neural network based on self-attention originally designed for natural language processing, have considerably surpassed previous convolutional or recurrent approaches in various vision processing tasks. Specifically, vision transformers offer robust, unified, and even simpler solutions for various segmentation tasks. This survey provides a thorough overview of transformer-based visual segmentation, summarizing recent advancements. We first review the background, encompassing problem definitions, datasets, and prior convolutional methods. Next, we summarize a meta-architecture that unifies all recent transformer-based approaches. Based on this meta-architecture, we examine various method designs, including modifications to the meta-architecture and associated applications. We also present several closely related settings, including 3D point cloud segmentation, foundation model tuning, domain-aware segmentation, efficient segmentation, and medical segmentation. Additionally, we compile and re-evaluate the reviewed methods on several well-established datasets. Finally, we identify open challenges in this field and propose directions for future research. The project page can be found at https://github.com/lxtGH/Awesome-Segmentation-With-Transformer. We will also continually monitor developments in this rapidly evolving field.},
  archiveprefix = {arXiv},
  keywords = {Computer Science - Computer Vision and Pattern Recognition},
  file = {C\:\\Users\\steph\\Zotero\\storage\\WCQQXVML\\Li et al. - 2023 - Transformer-Based Visual Segmentation A Survey.pdf;C\:\\Users\\steph\\Zotero\\storage\\EZIFZG4B\\2304.html}
}

@inproceedings{li2024generalized,
  title = {Generalized {{Few-Shot Meets Remote Sensing}}: {{Discovering Novel Classes}} in {{Land Cover Mapping}} via {{Hybrid Semantic Segmentation Framework}}},
  shorttitle = {Generalized {{Few-Shot Meets Remote Sensing}}},
  booktitle = {Proceedings of the {{IEEE}}/{{CVF Conference}} on {{Computer Vision}} and {{Pattern Recognition}}},
  author = {Li, Zhuohong and Lu, Fangxiao and Zou, Jiaqi and Hu, Lei and Zhang, Hongyan},
  year = {2024},
  pages = {2744--2754},
  urldate = {2024-09-18},
  langid = {english},
  file = {C:\Users\steph\Zotero\storage\6IBDUGMW\Li et al. - 2024 - Generalized Few-Shot Meets Remote Sensing Discove.pdf}
}

@article{li2024segment,
  title = {Segment {{Anything Model Can Not Segment Anything}}: {{Assessing AI Foundation Model}}'s {{Generalizability}} in {{Permafrost Mapping}}},
  shorttitle = {Segment {{Anything Model Can Not Segment Anything}}},
  author = {Li, Wenwen and Hsu, Chia-Yu and Wang, Sizhe and Yang, Yezhou and Lee, Hyunho and Liljedahl, Anna and Witharana, Chandi and Yang, Yili and Rogers, Brendan M. and Arundel, Samantha T. and Jones, Matthew B. and McHenry, Kenton and Solis, Patricia},
  year = {2024},
  month = jan,
  journal = {Remote Sensing},
  volume = {16},
  number = {5},
  pages = {797},
  publisher = {Multidisciplinary Digital Publishing Institute},
  issn = {2072-4292},
  doi = {10.3390/rs16050797},
  urldate = {2024-07-24},
  abstract = {This paper assesses trending AI foundation models, especially emerging computer vision foundation models and their performance in natural landscape feature segmentation. While the term foundation model has quickly garnered interest from the geospatial domain, its definition remains vague. Hence, this paper will first introduce AI foundation models and their defining characteristics. Built upon the tremendous success achieved by Large Language Models (LLMs) as the foundation models for language tasks, this paper discusses the challenges of building foundation models for geospatial artificial intelligence (GeoAI) vision tasks. To evaluate the performance of large AI vision models, especially Meta's Segment Anything Model (SAM), we implemented different instance segmentation pipelines that minimize the changes to SAM to leverage its power as a foundation model. A series of prompt strategies were developed to test SAM's performance regarding its theoretical upper bound of predictive accuracy, zero-shot performance, and domain adaptability through fine-tuning. The analysis used two permafrost feature datasets, ice-wedge polygons and retrogressive thaw slumps because (1) these landform features are more challenging to segment than man-made features due to their complicated formation mechanisms, diverse forms, and vague boundaries; (2) their presence and changes are important indicators for Arctic warming and climate change. The results show that although promising, SAM still has room for improvement to support AI-augmented terrain mapping. The spatial and domain generalizability of this finding is further validated using a more general dataset EuroCrops for agricultural field mapping. Finally, we discuss future research directions that strengthen SAM's applicability in challenging geospatial domains.},
  copyright = {http://creativecommons.org/licenses/by/3.0/},
  langid = {english},
  keywords = {artificial intelligence,foundation model,GeoAI,mapping,segmentation,zero-shot},
  file = {C:\Users\steph\Zotero\storage\SAU8LH8F\Li et al. - 2024 - Segment Anything Model Can Not Segment Anything A.pdf}
}

@article{liang2023localglobal,
  title = {Local-{{Global Context Aware Transformer}} for {{Language-Guided Video Segmentation}}},
  author = {Liang, Chen and Wang, Wenguan and Zhou, Tianfei and Miao, Jiaxu and Luo, Yawei and Yang, Yi},
  year = {2023},
  month = aug,
  journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
  volume = {45},
  number = {8},
  pages = {10055--10069},
  issn = {1939-3539},
  doi = {10.1109/TPAMI.2023.3262578},
  urldate = {2024-04-06},
  abstract = {We explore the task of language-guided video segmentation (LVS). Previous algorithms mostly adopt 3D CNNs to learn video representation, struggling to capture long-term context and easily suffering from visual-linguistic misalignment. In light of this, we present Locater (local-global context aware Transformer), which augments the Transformer architecture with a finite memory so as to query the entire video with the language expression in an efficient manner. The memory is designed to involve two components -- one for persistently preserving global video content, and one for dynamically gathering local temporal context and segmentation history. Based on the memorized local-global context and the particular content of each frame, Locater holistically and flexibly comprehends the expression as an adaptive query vector for each frame. The vector is used to query the corresponding frame for mask generation. The memory also allows Locater to process videos with linear time complexity and constant size memory, while Transformer-style self-attention computation scales quadratically with sequence length. To thoroughly examine the visual grounding capability of LVS models, we contribute a new LVS dataset, A2D-S{$^+$}+, which is built upon A2D-S dataset but poses increased challenges in disambiguating among similar objects. Experiments on three LVS datasets and our A2D-S{$^+$}+ show that Locater outperforms previous state-of-the-arts. Further, we won the 1st place in the Referring Video Object Segmentation Track of the 3rd Large-scale Video Object Segmentation Challenge, where Locater served as the foundation for the winning solution.},
  keywords = {Grounding,Language-guided video segmentation,Linguistics,memory network,multi-modal transformer,Object segmentation,Task analysis,Three-dimensional displays,Transformers,Visualization},
  file = {C:\Users\steph\Zotero\storage\GP5WMJH2\Liang et al. - 2023 - Local-Global Context Aware Transformer for Languag.pdf}
}

@inproceedings{lopez-paz2017gradient,
  title = {Gradient Episodic Memory for Continual Learning},
  booktitle = {Advances in {{Neural Information Processing Systems}}},
  author = {{Lopez-Paz}, David and others},
  year = {2017},
  pages = {6467--6476}
}

@article{lv2023deep,
  title = {Deep Learning-Based Semantic Segmentation of Remote Sensing Images: A Review},
  shorttitle = {Deep Learning-Based Semantic Segmentation of Remote Sensing Images},
  author = {Lv, Jinna and Shen, Qi and Lv, Mingzheng and Li, Yiran and Shi, Lei and Zhang, Peiying},
  year = {2023},
  month = jul,
  journal = {Frontiers in Ecology and Evolution},
  volume = {11},
  publisher = {Frontiers},
  issn = {2296-701X},
  doi = {10.3389/fevo.2023.1201125},
  urldate = {2024-06-12},
  abstract = {Semantic segmentation is a fundamental but challenging problem of pixel-level Remote Sensing (RS) data analysis. Semantic segmentation tasks based on aerial and satellite images play an important role in a wide range of applications. Recently, with the successful applications of Deep Learning (DL) in the Computer Vision (CV) field, more and more researchers have introduced and improved DL methods to the task of RS data semantic segmentation and achieved excellent results. Although there are a large number of DL methods, there is still a lack of method review and progress on semantic segmentation of RS data. To solve the problem, this paper surveys more than 100 papers in this field in the past five years and elaborates in detail on the aspects of technical framework classification discussion, datasets, experimental evaluation, research challenges, and future research directions. Different from several previously published surveys, this paper first focuses on comprehensively summarizing the advantages and disadvantages of techniques models according to the important and difficult points. This research will help beginners quickly establish research ideas and processes in this field, enabling them to focus on algorithm innovation without paying too much attention to datasets, evaluation indicators, and research frameworks.},
  langid = {english},
  keywords = {Convolutional Neural Network,deep learning,remote sensing,satellite image,Sematic segmentation},
  file = {C:\Users\steph\Zotero\storage\JHRCUPIT\Lv et al. - 2023 - Deep learning-based semantic segmentation of remot.pdf}
}

@inproceedings{marczak2025revisiting,
  title = {Revisiting {{Supervision}} for~{{Continual Representation Learning}}},
  booktitle = {Computer {{Vision}} -- {{ECCV}} 2024},
  author = {Marczak, Daniel and Cygert, Sebastian and Trzci{\'n}ski, Tomasz and Twardowski, Bart{\l}omiej},
  editor = {Leonardis, Ale{\v s} and Ricci, Elisa and Roth, Stefan and Russakovsky, Olga and Sattler, Torsten and Varol, G{\"u}l},
  year = {2025},
  pages = {181--197},
  publisher = {Springer Nature Switzerland},
  address = {Cham},
  doi = {10.1007/978-3-031-72658-3_11},
  abstract = {In the field of continual learning, models are designed to learn tasks one after the other. While most research has centered on supervised continual learning, there is a growing interest in unsupervised continual learning, which makes use of the vast amounts of unlabeled data. Recent studies have highlighted the strengths of unsupervised methods, particularly self-supervised learning, in providing robust representations. The improved transferability of those representations built with self-supervised methods is often associated with the role played by the multi-layer perceptron projector. In this work, we depart from this observation and reexamine the role of supervision in continual representation learning. We reckon that additional information, such as human annotations, should not deteriorate the quality of representations. Our findings show that supervised models when enhanced with a multi-layer perceptron head, can outperform self-supervised models in continual representation learning. This highlights the importance of the multi-layer perceptron projector in shaping feature transferability across a sequence of tasks in continual learning. The code is available on github.},
  isbn = {978-3-031-72658-3},
  langid = {english}
}

@article{marsocci2023continual,
  title = {Continual {{Barlow Twins}}: {{Continual Self-Supervised Learning}} for {{Remote Sensing Semantic Segmentation}}},
  shorttitle = {Continual {{Barlow Twins}}},
  author = {Marsocci, Valerio and Scardapane, Simone},
  year = {2023},
  journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
  volume = {16},
  pages = {5049--5060},
  issn = {2151-1535},
  doi = {10.1109/JSTARS.2023.3280029},
  abstract = {In the field of earth observation (EO), continual learning (CL) algorithms have been proposed to deal with large datasets by decomposing them into several subsets and processing them incrementally. The majority of these algorithms assume that data are, first, coming from a single source, and second, fully labeled. Real-world EO datasets are instead characterized by a large heterogeneity (e.g., coming from aerial, satellite, or drone scenarios), and for the most part they are unlabeled, meaning they can be fully exploited only through the emerging self-supervised learning (SSL) paradigm. For these reasons, in this article, we present a new algorithm for merging SSL and CL for remote sensing applications that we call continual Barlow twins. It combines the advantages of one of the simplest self-supervision techniques, i.e., Barlow twins, with the elastic weight consolidation method to avoid catastrophic forgetting. In addition, we evaluate the proposed continual SSL approach on a highly heterogeneous EO dataset, showing the effectiveness of this strategy on a novel combination of three almost non-overlapping domains datasets (airborne Potsdam, satellite US3D, and drone unmanned aerial vehicle semantic segmentation dataset), on a crucial downstream task in EO, i.e., semantic segmentation. Encouraging results show the superiority of SSL in this setting, and the effectiveness of creating an incremental effective pretrained feature extractor, based on ResNet50, without the need of relying on the complete availability of all the data, with a valuable saving of time and resources.},
  keywords = {Continual learning (CL),Drones,Electo-optic effects,Feature extraction,remote sensing,Remote sensing,Satellites,self-supervised learning (SSL),semantic segmentation,Semantic segmentation,Task analysis},
  file = {C\:\\Users\\steph\\Zotero\\storage\\2575B9CI\\Marsocci et Scardapane - 2023 - Continual Barlow Twins Continual Self-Supervised .pdf;C\:\\Users\\steph\\Zotero\\storage\\X3Z3SDPV\\10135093.html}
}

@article{mcdonnell2023ranpac,
  title = {{{RanPAC}}: {{Random Projections}} and {{Pre-trained Models}} for {{Continual Learning}}},
  shorttitle = {{{RanPAC}}},
  author = {McDonnell, Mark D. and Gong, Dong and Parvaneh, Amin and Abbasnejad, Ehsan and {van den Hengel}, Anton},
  year = {2023},
  month = dec,
  journal = {Advances in Neural Information Processing Systems},
  volume = {36},
  pages = {12022--12053},
  urldate = {2024-04-15},
  langid = {english},
  file = {C:\Users\steph\Zotero\storage\YM5QIWVA\McDonnell et al. - 2023 - RanPAC Random Projections and Pre-trained Models .pdf}
}

@inproceedings{ostapenko2022continual,
  title = {Continual {{Learning}} with {{Foundation Models}}: {{An Empirical Study}} of {{Latent Replay}}},
  shorttitle = {Continual {{Learning}} with {{Foundation Models}}},
  booktitle = {Proceedings of {{The}} 1st {{Conference}} on {{Lifelong Learning Agents}}},
  author = {Ostapenko, Oleksiy and Lesort, Timothee and Rodriguez, Pau and Arefin, Md Rifat and Douillard, Arthur and Rish, Irina and Charlin, Laurent},
  year = {2022},
  month = nov,
  pages = {60--91},
  publisher = {PMLR},
  issn = {2640-3498},
  urldate = {2024-01-05},
  abstract = {Rapid development of large-scale pre-training has resulted in foundation models that can act as effective feature extractors on a variety of downstream tasks and domains. Motivated by this, we study the efficacy of pre-trained vision models as a foundation for downstream continual learning (CL) scenarios. Our goal is twofold. First, we want to understand the compute-accuracy trade-off between CL in the raw-data space and in the latent space of pre-trained encoders. Second, we investigate how the characteristics of the encoder, the pre-training algorithm and data, as well as of the resulting latent space affect CL performance. For this, we compare the efficacy of various pre-trained models in large-scale benchmarking scenarios with a vanilla replay setting applied in the latent and in the raw-data space. Notably, this study shows how transfer, forgetting, task similarity and learning are dependent on the input data characteristics and not necessarily on the CL algorithms. First, we show that under some circumstances reasonable CL performance can readily be achieved with a non-parametric classifier at negligible compute. We then show how models pre-trained on broader data result in better performance for various replay sizes. We explain this with representational similarity and transfer properties of these representations. Finally, we show the effectiveness of self-supervised pre-training for downstream domains that are out-of-distribution as compared to the pre-training domain. We point out and validate several research directions that can further increase the efficacy of latent CL including representation ensembling. The diverse set of datasets used in this study can serve as a compute-efficient playground for further CL research. We will publish the code.},
  langid = {english},
  file = {C\:\\Users\\steph\\Zotero\\storage\\VK9GSIE9\\Ostapenko et al. - 2022 - Continual Learning with Foundation Models An Empi.pdf;C\:\\Users\\steph\\Zotero\\storage\\X94QJX6G\\Ostapenko et al. - 2022 - Continual Learning with Foundation Models An Empi.pdf}
}

@inproceedings{panos2023first,
  title = {First {{Session Adaptation}}: {{A Strong Replay-Free Baseline}} for {{Class-Incremental Learning}}},
  shorttitle = {First {{Session Adaptation}}},
  booktitle = {Proceedings of the {{IEEE}}/{{CVF International Conference}} on {{Computer Vision}}},
  author = {Panos, Aristeidis and Kobe, Yuriko and Reino, Daniel Olmeda and Aljundi, Rahaf and Turner, Richard E.},
  year = {2023},
  pages = {18820--18830},
  urldate = {2024-04-15},
  langid = {english},
  file = {C:\Users\steph\Zotero\storage\HG77RLFX\Panos et al. - 2023 - First Session Adaptation A Strong Replay-Free Bas.pdf}
}

@article{patel2015visual,
  title = {Visual Domain Adaptation: {{A}} Survey of Recent Advances},
  author = {Patel, Vishal M and Gopalan, Raghuraman and Li, Ruonan and Chellappa, Rama},
  year = {2015},
  journal = {IEEE signal processing magazine},
  volume = {32},
  number = {3},
  pages = {53--69},
  publisher = {IEEE},
  keywords = {Classification algorithms,Computer vision,Pattern recognition,Semisupervised learning,Signal processing algorithms,Training data,Visualization},
  file = {C\:\\Users\\steph\\Zotero\\storage\\47NKY9HM\\Patel et al. - 2015 - Visual Domain Adaptation A survey of recent advan.pdf;C\:\\Users\\steph\\Zotero\\storage\\DCE8GH6V\\7078994.html}
}

@inproceedings{peng2019moment,
  title = {Moment {{Matching}} for {{Multi-Source Domain Adaptation}}},
  booktitle = {Proceedings of the {{IEEE}}/{{CVF International Conference}} on {{Computer Vision}}},
  author = {Peng, Xingchao and Bai, Qinxun and Xia, Xide and Huang, Zijun and Saenko, Kate and Wang, Bo},
  year = {2019},
  pages = {1406--1415},
  urldate = {2025-03-08},
  file = {C:\Users\steph\Zotero\storage\K225YRVH\Peng et al. - 2019 - Moment Matching for Multi-Source Domain Adaptation.pdf}
}

@misc{peng2025tsvd,
  title = {{{TSVD}}: {{Bridging Theory}} and {{Practice}} in {{Continual Learning}} with {{Pre-trained Models}}},
  shorttitle = {{{TSVD}}},
  author = {Peng, Liangzu and Elenter, Juan and Agterberg, Joshua and Ribeiro, Alejandro and Vidal, Ren{\'e}},
  year = {2025},
  month = mar,
  number = {arXiv:2410.00645},
  eprint = {2410.00645},
  primaryclass = {cs},
  publisher = {arXiv},
  doi = {10.48550/arXiv.2410.00645},
  urldate = {2025-03-09},
  abstract = {The goal of continual learning (CL) is to train a model that can solve multiple tasks presented sequentially. Recent CL approaches have achieved strong performance by leveraging large pre-trained models that generalize well to downstream tasks. However, such methods lack theoretical guarantees, making them prone to unexpected failures. Conversely, principled CL approaches often fail to achieve competitive performance. In this work, we aim to bridge this gap between theory and practice by designing a simple CL method that is theoretically sound and highly performant. Specifically, we lift pre-trained features into a higher dimensional space and formulate an over-parametrized minimum-norm least-squares problem. We find that the lifted features are highly ill-conditioned, potentially leading to large training errors (numerical instability) and increased generalization errors. We address these challenges by continually truncating the singular value decomposition (SVD) of the lifted features. Our approach, termed TSVD, is stable with respect to the choice of hyperparameters, can handle hundreds of tasks, and outperforms state-of-the-art CL methods on multiple datasets. Importantly, our method satisfies a recurrence relation throughout its continual learning process, which allows us to prove it maintains small training and generalization errors by appropriately truncating a fraction of SVD factors. This results in a stable continual learning method with strong empirical performance and theoretical guarantees. Code available: https://github.com/liangzu/tsvd.},
  archiveprefix = {arXiv},
  keywords = {Computer Science - Machine Learning},
  file = {C\:\\Users\\steph\\Zotero\\storage\\8S9NFLHC\\Peng et al. - 2025 - TSVD Bridging Theory and Practice in Continual Learning with Pre-trained Models.pdf;C\:\\Users\\steph\\Zotero\\storage\\QVL56ZDS\\2410.html}
}



@inproceedings{radford2021learning,
  title = {Learning {{Transferable Visual Models From Natural Language Supervision}}},
  booktitle = {Proceedings of the 38th {{International Conference}} on {{Machine Learning}}},
  author = {Radford, Alec and Kim, Jong Wook and Hallacy, Chris and Ramesh, Aditya and Goh, Gabriel and Agarwal, Sandhini and Sastry, Girish and Askell, Amanda and Mishkin, Pamela and Clark, Jack and Krueger, Gretchen and Sutskever, Ilya},
  year = {2021},
  month = jul,
  pages = {8748--8763},
  publisher = {PMLR},
  issn = {2640-3498},
  urldate = {2022-11-04},
  abstract = {State-of-the-art computer vision systems are trained to predict a fixed set of predetermined object categories. This restricted form of supervision limits their generality and usability since additional labeled data is needed to specify any other visual concept. Learning directly from raw text about images is a promising alternative which leverages a much broader source of supervision. We demonstrate that the simple pre-training task of predicting which caption goes with which image is an efficient and scalable way to learn SOTA image representations from scratch on a dataset of 400 million (image, text) pairs collected from the internet. After pre-training, natural language is used to reference learned visual concepts (or describe new ones) enabling zero-shot transfer of the model to downstream tasks. We study the performance of this approach by benchmarking on over 30 different existing computer vision datasets, spanning tasks such as OCR, action recognition in videos, geo-localization, and many types of fine-grained object classification. The model transfers non-trivially to most tasks and is often competitive with a fully supervised baseline without the need for any dataset specific training. For instance, we match the accuracy of the original ResNet-50 on ImageNet zero-shot without needing to use any of the 1.28 million training examples it was trained on.},
  langid = {english},
  file = {C\:\\Users\\steph\\Zotero\\storage\\2HBGSJY7\\Radford et al. - 2021 - Learning Transferable Visual Models From Natural L.pdf;C\:\\Users\\steph\\Zotero\\storage\\CK2IGQTT\\Radford et al. - 2021 - Learning Transferable Visual Models From Natural L.pdf;C\:\\Users\\steph\\Zotero\\storage\\VZRFDQM4\\Radford et al. - 2021 - Learning Transferable Visual Models From Natural L.pdf;C\:\\Users\\steph\\Zotero\\storage\\WSJ2S6PK\\Radford et al. - 2021 - Learning Transferable Visual Models From Natural L.pdf}
}

@article{rong2022historical,
  title = {Historical {{Information-Guided Class-Incremental Semantic Segmentation}} in {{Remote Sensing Images}}},
  author = {Rong, Xuee and Sun, Xian and Diao, Wenhui and Wang, Peijin and Yuan, Zhiqiang and Wang, Hongqi},
  year = {2022},
  journal = {IEEE Transactions on Geoscience and Remote Sensing},
  volume = {60},
  pages = {1--18},
  issn = {1558-0644},
  doi = {10.1109/TGRS.2022.3170349},
  urldate = {2024-06-19},
  abstract = {Despite the extraordinary success of the deep architectures on semantic segmentation for remote sensing (RS) images, they have difficulties in learning new classes from a sequential data stream because of catastrophic forgetting. Continual learning for semantic segmentation (CSS) is an emerging trend for its capability to cope with the above problems effectively. However, old classes from previous steps are collapsed into the background, which further aggravates the challenge of CSS in the RS scene. In this article, we revisit the knowledge distillation (KD) strategy and the characteristics of class-incremental semantic segmentation (CISS) and then present a generalized and effective framework to learn new classes while preserving knowledge of the learned classes. In particular, we propose two novel historical information-guided modules: the feature global perception module and the label reconstruction (LR) module. The former enables the current model to pay more attention to the region related to the old categories identified by the historical information when learning new classes. Meanwhile, the latter retrieves pixels belonging to the learned classes from the background to handle the background shift problem and maintain the high performance of old classes. We have conducted comprehensive experiments on two RS semantic segmentation datasets of Instance Segmentation in Aerial Images Dataset (iSAID) and Gao Fen (GF) challenge semantic segmentation dataset (GCSS). The experimental results outperform the current state-of-the-art methods in most incremental settings, which demonstrates the effectiveness of the proposed framework.},
  keywords = {Catastrophic forgetting (CF),continual learning,Data models,Image reconstruction,Image segmentation,remote sensing (RS),semantic segmentation,Semantics,Task analysis,Training,Transformers},
  file = {C:\Users\steph\Zotero\storage\J6K252LK\Rong et al. - 2022 - Historical Information-Guided Class-Incremental Se.pdf}
}

@article{rong2023micro,
  title = {{{MiCro}}: {{Modeling Cross-Image Semantic Relationship Dependencies}} for {{Class-Incremental Semantic Segmentation}} in {{Remote Sensing Images}}},
  shorttitle = {{{MiCro}}},
  author = {Rong, Xuee and Wang, Peijin and Diao, Wenhui and Yang, Yiran and Yin, Wenxin and Zeng, Xuan and Wang, Hongqi and Sun, Xian},
  year = {2023},
  journal = {IEEE Transactions on Geoscience and Remote Sensing},
  volume = {61},
  pages = {1--18},
  issn = {1558-0644},
  doi = {10.1109/TGRS.2023.3297203},
  urldate = {2024-06-19},
  abstract = {Continual learning is an effective way to overcome catastrophic forgetting (CF) in incremental learning for semantic segmentation. The existing continual semantic segmentation (CSS) methods of remote sensing (RS) ignore the semantic relationships among pixels across different images, which will lead to disappointing segmentation results, such as edge pixel misclassification and small object omission. In this article, we propose a framework for modeling cross-image semantic relationship dependencies (MiCro), which aims to learn an interclass separable and intraclass cohesive feature space from the pixel relationships across various images to ensure that learned categories can prevent CF in the incremental process. Specifically, we exploit the relationships among pixels of images in minibatch to construct three losses: 1) cross-image feature relationship distillation (CFRD) loss, which builds a well-structured feature space; 2) cross-image intraclass feature cohesion (CIFC) loss, which is devised to make intraclass features more cohesive; and 3) cross-image class-area weighted cross-entropy (CCWCE) loss, which is mainly employed to inversely weight the proportion of category area in minibatch. The effectiveness of the proposed approach is demonstrated by extensive experiments on three RS semantic segmentation datasets from ISPRS Vaihingen, ISPRS Potsdam, and iSAID. MiCro is superior to the current most advanced methods in most incremental settings, especially improving mIoU by 11.59\% on ISPRS Vaihingen, 13.17\% on ISPRS Potsdam, and 15.01\% on iSAID in the most difficult incremental settings, which promotes the CSS to a state-of-the-art (SOTA) level. The code will be available at https://github.com/RongXueE/MiCro.},
  keywords = {Catastrophic forgetting (CF),class-incremental learning (CIL),Data models,Knowledge engineering,Remote sensing,remote sensing (RS),semantic segmentation,Semantic segmentation,Semantics,Task analysis,Training},
  file = {C:\Users\steph\Zotero\storage\HD65MR5T\Rong et al. - 2023 - MiCro Modeling Cross-Image Semantic Relationship .pdf}
}

@inproceedings{saporta2022multihead,
  title = {Multi-{{Head Distillation}} for {{Continual Unsupervised Domain Adaptation}} in {{Semantic Segmentation}}},
  booktitle = {Proceedings of the {{IEEE}}/{{CVF Conference}} on {{Computer Vision}} and {{Pattern Recognition}}},
  author = {Saporta, Antoine and Douillard, Arthur and Vu, Tuan-Hung and P{\'e}rez, Patrick and Cord, Matthieu},
  year = {2022},
  pages = {3751--3760},
  urldate = {2023-02-22},
  langid = {english},
  file = {C\:\\Users\\steph\\Zotero\\storage\\68B3HKST\\Saporta et al. - 2022 - Multi-Head Distillation for Continual Unsupervised.pdf;C\:\\Users\\steph\\Zotero\\storage\\ZW2NHG43\\Saporta et al. - 2022 - Multi-Head Distillation for Continual Unsupervised.pdf}
}

@article{scholkopf2021causal,
  title = {Toward {{Causal Representation Learning}}},
  author = {Sch{\"o}lkopf, Bernhard and Locatello, Francesco and Bauer, Stefan and Ke, Nan Rosemary and Kalchbrenner, Nal and Goyal, Anirudh and Bengio, Yoshua},
  year = {2021},
  month = may,
  journal = {Proceedings of the IEEE},
  volume = {109},
  number = {5},
  pages = {612--634},
  issn = {1558-2256},
  doi = {10.1109/JPROC.2021.3058954},
  abstract = {The two fields of machine learning and graphical causality arose and are developed separately. However, there is, now, cross-pollination and increasing interest in both fields to benefit from the advances of the other. In this article, we review fundamental concepts of causal inference and relate them to crucial open problems of machine learning, including transfer and generalization, thereby assaying how causality can contribute to modern machine learning research. This also applies in the opposite direction: we note that most work in causality starts from the premise that the causal variables are given. A central problem for AI and causality is, thus, causal representation learning, that is, the discovery of high-level causal variables from low-level observations. Finally, we delineate some implications of causality for machine learning and propose key research areas at the intersection of both communities.},
  keywords = {Adaptation models,Artificial intelligence,causality,Data models,deep learning,Differential equations,Inference algorithms,Machine learning,Mathematical model,representation learning,Representation learning,Training data},
  file = {C:\Users\steph\Zotero\storage\4R2PV92A\Schölkopf et al. - 2021 - Toward Causal Representation Learning.pdf}
}

@inproceedings{schott2022visual,
  title = {Visual {{Representation Learning Does Not Generalize Strongly Within}} the {{Same Domain}}},
  booktitle = {International {{Conference}} on {{Learning Representations}}},
  author = {Schott, Lukas and K{\"u}gelgen, Julius Von and Tr{\"a}uble, Frederik and Gehler, Peter Vincent and Russell, Chris and Bethge, Matthias and Sch{\"o}lkopf, Bernhard and Locatello, Francesco and Brendel, Wieland},
  year = {2022},
  month = feb,
  urldate = {2022-12-05},
  abstract = {An important component for generalization in machine learning is to uncover underlying latent factors of variation as well as the mechanism through which each factor acts in the world. In this paper, we test whether 17 unsupervised, weakly supervised, and fully supervised representation learning approaches correctly infer the generative factors of variation in simple datasets (dSprites, Shapes3D, MPI3D) from controlled environments, and on our contributed CelebGlow dataset. In contrast to prior robustness work that introduces novel factors of variation during test time, such as blur or other (un)structured noise, we here recompose, interpolate, or extrapolate only existing factors of variation from the training data set (e.g., small and medium-sized objects during training and large objects during testing). Models that learn the correct mechanism should be able to generalize to this benchmark. In total, we train and test 2000+ models and observe that all of them struggle to learn the underlying mechanism regardless of supervision signal and architectural bias. Moreover, the generalization capabilities of all tested models drop significantly as we move from artificial datasets towards more realistic real-world datasets. Despite their inability to identify the correct mechanism, the models are quite modular as their ability to infer other in-distribution factors remains fairly stable, providing only a single factor is out-of-distribution. These results point to an important yet understudied problem of learning mechanistic models of observations that can facilitate generalization.},
  langid = {english},
  file = {C\:\\Users\\steph\\Zotero\\storage\\SPAQBDAS\\Schott et al. - 2022 - Visual Representation Learning Does Not Generalize.pdf;C\:\\Users\\steph\\Zotero\\storage\\QZV9UTLB\\forum.html}
}

@article{shan2022classincremental,
  title = {Class-{{Incremental Learning}} for {{Semantic Segmentation}} in {{Aerial Imagery}} via {{Distillation}} in {{All Aspects}}},
  author = {Shan, Lianlei and Wang, Weiqiang and Lv, Ke and Luo, Bin},
  year = {2022},
  journal = {IEEE Transactions on Geoscience and Remote Sensing},
  volume = {60},
  pages = {1--12},
  issn = {1558-0644},
  doi = {10.1109/TGRS.2021.3135456},
  urldate = {2024-06-19},
  abstract = {Incremental learning using neural networks achieves great success in semantic segmentation but still suffers from catastrophic forgetting. In this article, we propose an effective class-incremental segmentation method without storing old data. To alleviate the issue of forgetting, we present two important modules, i.e., the deep feature distillation (DFD) module and the label mixed (LM) module. The DFD module is established to learn a good feature representation of old classes by distilling a new compact feature representation from different layers of networks. The proposed LM module first identifies the examples (pixels) of old classes with high confidences utilizing the output of old models, and then, they are combined with examples of new classes to supervise the training of new models, which can achieve a good balance between learning new classing and avoiding forgetting old ones. Our ablation studies show that the DFD module and the LM module can make the learning network obtain 6.2\% and 15\% performance gains [mean Intersection over Union (mIOU)], respectively. Furthermore, by introducing the supervision of output distillation loss, we compare our method with several state-of-the-art methods in the extensive experiments, and the experimental results all show that our method is significantly superior to them on the dataset of aerial images.},
  keywords = {Class-incremental segmentation,Computational modeling,Data models,deep feature distillation (DFD) module,Image segmentation,incremental learning,Knowledge engineering,label mixed (LM) module,semantic segmentation,Semantics,Task analysis,Training},
  file = {C:\Users\steph\Zotero\storage\EZTNT2PY\Shan et al. - 2022 - Class-Incremental Learning for Semantic Segmentati.pdf}
}

@inproceedings{shang2023incrementer,
  title = {Incrementer: {{Transformer}} for {{Class-Incremental Semantic Segmentation With Knowledge Distillation Focusing}} on {{Old Class}}},
  shorttitle = {Incrementer},
  booktitle = {Proceedings of the {{IEEE}}/{{CVF Conference}} on {{Computer Vision}} and {{Pattern Recognition}}},
  author = {Shang, Chao and Li, Hongliang and Meng, Fanman and Wu, Qingbo and Qiu, Heqian and Wang, Lanxiao},
  year = {2023},
  pages = {7214--7224},
  urldate = {2024-06-19},
  langid = {english},
  file = {C:\Users\steph\Zotero\storage\D7935VVY\Shang et al. - 2023 - Incrementer Transformer for Class-Incremental Sem.pdf}
}

@article{shenaj2022continual,
  title = {Continual Coarse-to-Fine Domain Adaptation in Semantic Segmentation},
  author = {Shenaj, Donald and Barbato, Francesco and Michieli, Umberto and Zanuttigh, Pietro},
  year = {2022},
  month = may,
  journal = {Image and Vision Computing},
  volume = {121},
  pages = {104426},
  issn = {0262-8856},
  doi = {10.1016/j.imavis.2022.104426},
  urldate = {2024-06-19},
  abstract = {Deep neural networks are typically trained in a single shot for a specific task and data distribution, but in real world settings both the task and the domain of application can change. The problem becomes even more challenging in dense predictive tasks, such as semantic segmentation, and furthermore most approaches tackle the two problems separately. In this paper we introduce the novel task of coarse-to-fine learning of semantic segmentation architectures in presence of domain shift. We consider subsequent learning stages progressively refining the task at the semantic level; i.e., the finer set of semantic labels at each learning step is hierarchically derived from the coarser set of the previous step. We propose a new approach (CCDA) to tackle this scenario. First, we employ the maximum squares loss to align source and target domains and, at the same time, to balance the gradients between well-classified and harder samples. Second, we introduce a novel coarse-to-fine knowledge distillation constraint to transfer network capabilities acquired on a coarser set of labels to a set of finer labels. Finally, we design a coarse-to-fine weight initialization rule to spread the importance from each coarse class to the respective finer classes. To evaluate our approach, we design two benchmarks where source knowledge is extracted from the GTA5 dataset and it is transferred to either the Cityscapes or the IDD datasets, and we show how it outperforms the main competitors.},
  keywords = {Coarse-to-fine learning,Continual learning,Deep learning,Semantic segmentation,Unsupervised domain adaptation},
  file = {C:\Users\steph\Zotero\storage\5HK8ZEDJ\Shenaj et al. - 2022 - Continual coarse-to-fine domain adaptation in sema.pdf}
}

@inproceedings{singla2021understandinga,
  title = {Understanding {{Failures}} of {{Deep Networks}} via {{Robust Feature Extraction}}},
  booktitle = {Proceedings of the {{IEEE}}/{{CVF Conference}} on {{Computer Vision}} and {{Pattern Recognition}}},
  author = {Singla, Sahil and Nushi, Besmira and Shah, Shital and Kamar, Ece and Horvitz, Eric},
  year = {2021},
  pages = {12853--12862},
  urldate = {2022-10-21},
  langid = {english},
  annotation = {ZSCC: 0000022},
  file = {C\:\\Users\\steph\\Zotero\\storage\\SIJ7SQYC\\Singla et al. - 2021 - Understanding Failures of Deep Networks via Robust.pdf;C\:\\Users\\steph\\Zotero\\storage\\FWA8QA8U\\Singla_Understanding_Failures_of_Deep_Networks_via_Robust_Feature_Extraction_CVPR_2021_paper.html}
}

@article{song2023classprompting,
  title = {Class-{{Prompting Transformer}} for {{Incremental Semantic Segmentation}}},
  author = {Song, Zichen and Shi, Zhaofeng and Shang, Chao and Meng, Fanman and Xu, Linfeng},
  year = {2023},
  journal = {IEEE Access},
  volume = {11},
  pages = {100154--100164},
  issn = {2169-3536},
  doi = {10.1109/ACCESS.2023.3315327},
  urldate = {2024-06-19},
  abstract = {Class-incremental Semantic Segmentation (CISS) aims to learn new tasks sequentially that assign a specific category to each pixel of a given image while preserving the original capability to segment the old classes even if the labels of old tasks are absent. Most existing CISS methods suppress catastrophic forgetting by directly distilling on specific layers, which ignores the semantic gap between training data from the old and new classes with different distributions and leads to distillation errors, thus affecting segmentation performance. In this paper, we propose a Class-prompting Transformer (CPT) to introduce external prior knowledge provided by a pre-trained vision-language encoder into CISS pipelines for bridging the old and new classes and performing more generalized initialization and distillation. Specifically, we proposed a Prompt-guided Initialization Module (PIM), which measures the relationships between the class prompts and old query parameters to initialize the new query parameters for relocating the previous knowledge to the learning of new tasks. Then, a Semantic-aligned Distillation Module (SDM) is proposed to incorporate class prompt information with the class-aware embeddings extracted from the decoder to prevent the semantic gap problem between distinct class data and conduct adaptive knowledge transfer to suppress catastrophic forgetting. Extensive experiments on Pascal VOC and ADE20K datasets for the CISS task demonstrate the superiority of the proposed method, which achieves state-of-the-art performance.},
  keywords = {class prompt learning,Computational modeling,Decoding,Incremental semantic segmentation,knowledge distillation,Semantic segmentation,Semantics,Task analysis,Transformers,Visualization},
  file = {C:\Users\steph\Zotero\storage\LJ8NRZAC\Song et al. - 2023 - Class-Prompting Transformer for Incremental Semant.pdf}
}

@misc{sultan2024geosam,
  title = {{{GeoSAM}}: {{Fine-tuning SAM}} with {{Sparse}} and {{Dense Visual Prompting}} for {{Automated Segmentation}} of {{Mobility Infrastructure}}},
  shorttitle = {{{GeoSAM}}},
  author = {Sultan, Rafi Ibn and Li, Chengyin and Zhu, Hui and Khanduri, Prashant and Brocanelli, Marco and Zhu, Dongxiao},
  year = {2024},
  month = jan,
  number = {arXiv:2311.11319},
  eprint = {2311.11319},
  primaryclass = {cs},
  publisher = {arXiv},
  doi = {10.48550/arXiv.2311.11319},
  urldate = {2024-07-24},
  abstract = {The Segment Anything Model (SAM) has shown impressive performance when applied to natural image segmentation. However, it struggles with geographical images like aerial and satellite imagery, especially when segmenting mobility infrastructure including roads, sidewalks, and crosswalks. This inferior performance stems from the narrow features of these objects, their textures blending into the surroundings, and interference from objects like trees, buildings, vehicles, and pedestrians - all of which can disorient the model to produce inaccurate segmentation maps. To address these challenges, we propose Geographical SAM (GeoSAM), a novel SAM-based framework that implements a fine-tuning strategy using the dense visual prompt from zero-shot learning, and the sparse visual prompt from a pre-trained CNN segmentation model. The proposed GeoSAM outperforms existing approaches for geographical image segmentation, specifically by 26\%, 7\%, and 17\% for road infrastructure, pedestrian infrastructure, and on average, respectively, representing a momentous leap in leveraging foundation models to segment mobility infrastructure including both road and pedestrian infrastructure in geographical images. The source code can be found on this GitHub repository: https://github.com/rafiibnsultan/GeoSAM/tree/main.},
  archiveprefix = {arXiv},
  keywords = {Computer Science - Artificial Intelligence,Computer Science - Computer Vision and Pattern Recognition},
  file = {C\:\\Users\\steph\\Zotero\\storage\\7B69J73N\\Sultan et al. - 2024 - GeoSAM Fine-tuning SAM with Sparse and Dense Visu.pdf;C\:\\Users\\steph\\Zotero\\storage\\LDIIIJL3\\geosam_2311.11319v2.pdf;C\:\\Users\\steph\\Zotero\\storage\\K3SIQIFV\\2311.html}
}

@article{thisanke2023semantic,
  title = {Semantic Segmentation Using {{Vision Transformers}}: {{A}} Survey},
  shorttitle = {Semantic Segmentation Using {{Vision Transformers}}},
  author = {Thisanke, Hans and Deshan, Chamli and Chamith, Kavindu and Seneviratne, Sachith and Vidanaarachchi, Rajith and Herath, Damayanthi},
  year = {2023},
  month = nov,
  journal = {Engineering Applications of Artificial Intelligence},
  volume = {126},
  pages = {106669},
  issn = {0952-1976},
  doi = {10.1016/j.engappai.2023.106669},
  urldate = {2024-04-22},
  abstract = {Semantic segmentation has a broad range of applications in a variety of domains including land coverage analysis, autonomous driving, and medical image analysis. Convolutional neural networks (CNN) and Vision Transformers (ViTs) provide the architecture models for semantic segmentation. Even though ViTs have proven success in image classification, they cannot be directly applied to dense prediction tasks such as image segmentation and object detection since ViT is not a general purpose backbone due to its patch partitioning scheme. In this survey, we discuss some of the different ViT architectures that can be used for semantic segmentation and how their evolution managed the above-stated challenge. The rise of ViT and its performance with a high success rate motivated the community to slowly replace the traditional convolutional neural networks in various computer vision tasks. This survey aims to review and compare the performances of ViT architectures designed for semantic segmentation using benchmarking datasets. This will be worthwhile for the community to yield knowledge regarding the implementations carried out in semantic segmentation and to discover more efficient methodologies using ViTs.},
  keywords = {Convolution neural networks,Deep learning,Review,Self-supervised learning,Semantic segmentation,Survey,Vision Transformer},
  file = {C:\Users\steph\Zotero\storage\6EW24Q99\Thisanke et al. - 2023 - Semantic segmentation using Vision Transformers A.pdf}
}

@article{toldo2024learning,
  title = {Learning with {{Style}}: {{Continual Semantic Segmentation Across Tasks}} and {{Domains}}},
  shorttitle = {Learning with {{Style}}},
  author = {Toldo, Marco and Michieli, Umberto and Zanuttigh, Pietro},
  year = {2024},
  journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
  pages = {1--16},
  issn = {1939-3539},
  doi = {10.1109/TPAMI.2024.3397461},
  urldate = {2024-06-19},
  abstract = {Deep learning models dealing with image understanding in real-world settings must be able to adapt to a wide variety of tasks across different domains. Domain adaptation and class incremental learning deal with domain and task variability separately, whereas their unified solution is still an open problem. We tackle both facets of the problem together, taking into account the semantic shift within both input and label spaces. We start by formally introducing continual learning under task and domain shift. Then, we address the proposed setup by using style transfer techniques to extend knowledge across domains when learning incremental tasks and a robust distillation framework to effectively recollect task knowledge under incremental domain shift. The devised framework (LwS, Learning with Style) is able to generalize incrementally acquired task knowledge across all the domains encountered, proving to be robust against catastrophic forgetting. Extensive experimental evaluation on multiple autonomous driving datasets shows how the proposed method outperforms existing approaches, which prove to be ill-equipped to deal with continual semantic segmentation under both task and domain shift. The code is available at https://lttm.dei.unipd.it/paper data/LwS.},
  keywords = {Adaptation models,Continual Learning,Domain Adaptation,Object detection,Semantic segmentation,Semantic Segmentation,Semantics,Task analysis,Training,Training data},
  file = {C\:\\Users\\steph\\Zotero\\storage\\H32Z9BC4\\Toldo et al. - 2024 - Learning with Style Continual Semantic Segmentati.pdf;C\:\\Users\\steph\\Zotero\\storage\\YNFWRNHS\\authors.html}
}

@article{wang2021transformer,
  title = {Transformer {{Meets Convolution}}: {{A Bilateral Awareness Network}} for {{Semantic Segmentation}} of {{Very Fine Resolution Urban Scene Images}}},
  shorttitle = {Transformer {{Meets Convolution}}},
  author = {Wang, Libo and Li, Rui and Wang, Dongzhi and Duan, Chenxi and Wang, Teng and Meng, Xiaoliang},
  year = {2021},
  month = jan,
  journal = {Remote Sensing},
  volume = {13},
  number = {16},
  pages = {3065},
  publisher = {Multidisciplinary Digital Publishing Institute},
  doi = {10.3390/rs13163065},
  urldate = {2022-01-21},
  abstract = {Semantic segmentation from very fine resolution (VFR) urban scene images plays a significant role in several application scenarios including autonomous driving, land cover classification, urban planning, etc. However, the tremendous details contained in the VFR image, especially the considerable variations in scale and appearance of objects, severely limit the potential of the existing deep learning approaches. Addressing such issues represents a promising research field in the remote sensing community, which paves the way for scene-level landscape pattern analysis and decision making. In this paper, we propose a Bilateral Awareness Network which contains a dependency path and a texture path to fully capture the long-range relationships and fine-grained details in VFR images. Specifically, the dependency path is conducted based on the ResT, a novel Transformer backbone with memory-efficient multi-head self-attention, while the texture path is built on the stacked convolution operation. In addition, using the linear attention mechanism, a feature aggregation module is designed to effectively fuse the dependency features and texture features. Extensive experiments conducted on the three large-scale urban scene image segmentation datasets, i.e., ISPRS Vaihingen dataset, ISPRS Potsdam dataset, and UAVid dataset, demonstrate the effectiveness of our BANet. Specifically, a 64.6\% mIoU is achieved on the UAVid dataset.},
  copyright = {http://creativecommons.org/licenses/by/3.0/},
  langid = {english},
  keywords = {attention mechanism,remote sensing,transformer,urban scene segmentation},
  file = {C\:\\Users\\steph\\Zotero\\storage\\H2KR43T5\\Wang et al. - 2021 - Transformer Meets Convolution A Bilateral Awarene.pdf;C\:\\Users\\steph\\Zotero\\storage\\FZ2Q2BJL\\3065.html}
}

@misc{wang2022internimage,
  title = {{{InternImage}}: {{Exploring Large-Scale Vision Foundation Models}} with {{Deformable Convolutions}}},
  shorttitle = {{{InternImage}}},
  author = {Wang, Wenhai and Dai, Jifeng and Chen, Zhe and Huang, Zhenhang and Li, Zhiqi and Zhu, Xizhou and Hu, Xiaowei and Lu, Tong and Lu, Lewei and Li, Hongsheng and Wang, Xiaogang and Qiao, Yu},
  year = {2022},
  month = nov,
  number = {arXiv:2211.05778},
  eprint = {2211.05778},
  primaryclass = {cs},
  publisher = {arXiv},
  doi = {10.48550/arXiv.2211.05778},
  urldate = {2023-02-23},
  abstract = {Compared to the great progress of large-scale vision transformers (ViTs) in recent years, large-scale models based on convolutional neural networks (CNNs) are still in an early state. This work presents a new large-scale CNN-based foundation model, termed InternImage, which can obtain the gain from increasing parameters and training data like ViTs. Different from the recent CNNs that focus on large dense kernels, InternImage takes deformable convolution as the core operator, so that our model not only has the large effective receptive field required for downstream tasks such as detection and segmentation, but also has the adaptive spatial aggregation conditioned by input and task information. As a result, the proposed InternImage reduces the strict inductive bias of traditional CNNs and makes it possible to learn stronger and more robust patterns with large-scale parameters from massive data like ViTs. The effectiveness of our model is proven on challenging benchmarks including ImageNet, COCO, and ADE20K. It is worth mentioning that InternImage-H achieved a new record 65.4 mAP on COCO test-dev and 62.9 mIoU on ADE20K, outperforming current leading CNNs and ViTs. The code will be released at https://github.com/OpenGVLab/InternImage.},
  archiveprefix = {arXiv},
  keywords = {Computer Science - Computer Vision and Pattern Recognition},
  file = {C\:\\Users\\steph\\Zotero\\storage\\ICIH26ND\\Wang et al. - 2022 - InternImage Exploring Large-Scale Vision Foundati.pdf;C\:\\Users\\steph\\Zotero\\storage\\DTBIJKI7\\2211.html}
}

@inproceedings{wang2022sprompts,
  title = {S-{{Prompts Learning}} with {{Pre-trained Transformers}}: {{An Occam}}'s {{Razor}} for {{Domain Incremental Learning}}},
  shorttitle = {S-{{Prompts Learning}} with {{Pre-trained Transformers}}},
  booktitle = {Advances in {{Neural Information Processing Systems}}},
  author = {Wang, Yabin and Huang, Zhiwu and Hong, Xiaopeng},
  year = {2022},
  month = oct,
  urldate = {2022-12-19},
  abstract = {State-of-the-art deep neural networks are still struggling to address the catastrophic forgetting problem in continual learning. In this paper, we propose one simple paradigm (named as S-Prompting) and two concrete approaches to highly reduce the forgetting degree in one of the most typical continual learning scenarios, i.e., domain increment learning (DIL). The key idea of the paradigm is to learn prompts independently across domains with pre-trained transformers, avoiding the use of exemplars that commonly appear in conventional methods. This results in a win-win game where the prompting can achieve the best for each domain. The independent prompting across domains only requests one single cross-entropy loss for training and one simple K-NN operation as a domain identifier for inference. The learning paradigm derives an image prompt learning approach and a novel language-image prompt learning approach. Owning an excellent scalability (0.03\% parameter increase per domain), the best of our approaches achieves a remarkable relative improvement (an average of about 30\%) over the best of the state-of-the-art exemplar-free methods for three standard DIL tasks, and even surpasses the best of them relatively by about 6\% in average when they use exemplars. Source code is available at https://github.com/iamwangyabin/S-Prompts.},
  langid = {english},
  file = {C:\Users\steph\Zotero\storage\9Q9NHH4K\Wang et al. - 2022 - S-Prompts Learning with Pre-trained Transformers .pdf}
}

@misc{wang2023comprehensive,
  title = {A {{Comprehensive Survey}} of {{Continual Learning}}: {{Theory}}, {{Method}} and {{Application}}},
  shorttitle = {A {{Comprehensive Survey}} of {{Continual Learning}}},
  author = {Wang, Liyuan and Zhang, Xingxing and Su, Hang and Zhu, Jun},
  year = {2023},
  month = jan,
  number = {arXiv:2302.00487},
  eprint = {2302.00487},
  primaryclass = {cs},
  publisher = {arXiv},
  doi = {10.48550/arXiv.2302.00487},
  urldate = {2023-04-17},
  abstract = {To cope with real-world dynamics, an intelligent agent needs to incrementally acquire, update, accumulate, and exploit knowledge throughout its lifetime. This ability, known as continual learning, provides a foundation for AI systems to develop themselves adaptively. In a general sense, continual learning is explicitly limited by catastrophic forgetting, where learning a new task usually results in a dramatic performance drop of the old tasks. Beyond this, increasingly numerous advances have emerged in recent years that largely extend the understanding and application of continual learning. The growing and widespread interest in this direction demonstrates its realistic significance as well as complexity. In this work, we present a comprehensive survey of continual learning, seeking to bridge the basic settings, theoretical foundations, representative methods, and practical applications. Based on existing theoretical and empirical results, we summarize the general objectives of continual learning as ensuring a proper stability-plasticity trade-off and an adequate intra/inter-task generalizability in the context of resource efficiency. Then we provide a state-of-the-art and elaborated taxonomy, extensively analyzing how representative strategies address continual learning, and how they are adapted to particular challenges in various applications. Through an in-depth discussion of continual learning in terms of the current trends, cross-directional prospects and interdisciplinary connections with neuroscience, we believe that such a holistic perspective can greatly facilitate subsequent exploration in this field and beyond.},
  archiveprefix = {arXiv},
  keywords = {Computer Science - Artificial Intelligence,Computer Science - Computer Vision and Pattern Recognition,Computer Science - Machine Learning},
  file = {C\:\\Users\\steph\\Zotero\\storage\\T8FA7GYB\\Wang et al. - 2023 - A Comprehensive Survey of Continual Learning Theo.pdf;C\:\\Users\\steph\\Zotero\\storage\\A97XJJ5E\\2302.html}
}

@misc{wang2023samrs,
  title = {{{SAMRS}}: {{Scaling-up Remote Sensing Segmentation Dataset}} with {{Segment Anything Model}}},
  shorttitle = {{{SAMRS}}},
  author = {Wang, Di and Zhang, Jing and Du, Bo and Xu, Minqiang and Liu, Lin and Tao, Dacheng and Zhang, Liangpei},
  year = {2023},
  month = oct,
  number = {arXiv:2305.02034},
  eprint = {2305.02034},
  primaryclass = {cs},
  publisher = {arXiv},
  doi = {10.48550/arXiv.2305.02034},
  urldate = {2023-11-21},
  abstract = {The success of the Segment Anything Model (SAM) demonstrates the significance of data-centric machine learning. However, due to the difficulties and high costs associated with annotating Remote Sensing (RS) images, a large amount of valuable RS data remains unlabeled, particularly at the pixel level. In this study, we leverage SAM and existing RS object detection datasets to develop an efficient pipeline for generating a large-scale RS segmentation dataset, dubbed SAMRS. SAMRS totally possesses 105,090 images and 1,668,241 instances, surpassing existing high-resolution RS segmentation datasets in size by several orders of magnitude. It provides object category, location, and instance information that can be used for semantic segmentation, instance segmentation, and object detection, either individually or in combination. We also provide a comprehensive analysis of SAMRS from various aspects. Moreover, preliminary experiments highlight the importance of conducting segmentation pre-training with SAMRS to address task discrepancies and alleviate the limitations posed by limited training data during fine-tuning. The code and dataset will be available at https://github.com/ViTAE-Transformer/SAMRS.},
  archiveprefix = {arXiv},
  keywords = {Computer Science - Computer Vision and Pattern Recognition},
  file = {C\:\\Users\\steph\\Zotero\\storage\\X69PCDBK\\Wang et al. - 2023 - SAMRS Scaling-up Remote Sensing Segmentation Data.pdf;C\:\\Users\\steph\\Zotero\\storage\\8463SW2B\\2305.html}
}

@article{wang2024comprehensive,
  title = {A {{Comprehensive Survey}} of {{Continual Learning}}: {{Theory}}, {{Method}} and {{Application}}},
  shorttitle = {A {{Comprehensive Survey}} of {{Continual Learning}}},
  author = {Wang, Liyuan and Zhang, Xingxing and Su, Hang and Zhu, Jun},
  year = {2024},
  journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
  pages = {1--20},
  issn = {1939-3539},
  doi = {10.1109/TPAMI.2024.3367329},
  urldate = {2024-03-13},
  abstract = {To cope with real-world dynamics, an intelligent system needs to incrementally acquire, update, accumulate, and exploit knowledge throughout its lifetime. This ability, known as continual learning, provides a foundation for AI systems to develop themselves adaptively. In a general sense, continual learning is explicitly limited by catastrophic forgetting, where learning a new task usually results in a dramatic performance drop of the old tasks. Beyond this, increasingly numerous advances have emerged in recent years that largely extend the understanding and application of continual learning. The growing and widespread interest in this direction demonstrates its realistic significance as well as complexity. In this work, we present a comprehensive survey of continual learning, seeking to bridge the basic settings, theoretical foundations, representative methods, and practical applications. Based on existing theoretical and empirical results, we summarize the general objectives of continual learning as ensuring a proper stability-plasticity trade-off and an adequate intra/inter-task generalizability in the context of resource efficiency. Then we provide a state-of-the-art and elaborated taxonomy, extensively analyzing how representative strategies address continual learning, and how they are adapted to particular challenges in various applications. Through an in-depth discussion of promising directions, we believe that such a holistic perspective can greatly facilitate subsequent exploration in this field and beyond.},
  keywords = {catastrophic forgetting,Complexity theory,Continual learning,incremental learning,lifelong learning,Stability analysis,Surveys,Task analysis,Testing,Training,Visualization},
  file = {C:\Users\steph\Zotero\storage\U8HUTTDR\Wang et al. - 2024 - A Comprehensive Survey of Continual Learning Theo.pdf}
}

@article{wang2024mtp,
  title = {{{MTP}}: {{Advancing Remote Sensing Foundation Model}} via {{Multitask Pretraining}}},
  shorttitle = {{{MTP}}},
  author = {Wang, Di and Zhang, Jing and Xu, Minqiang and Liu, Lin and Wang, Dongsheng and Gao, Erzhong and Han, Chengxi and Guo, Haonan and Du, Bo and Tao, Dacheng and Zhang, Liangpei},
  year = {2024},
  journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
  volume = {17},
  pages = {11632--11654},
  issn = {2151-1535},
  doi = {10.1109/JSTARS.2024.3408154},
  urldate = {2024-07-24},
  abstract = {Foundation models have reshaped the landscape of remote sensing (RS) by enhancing various image interpretation tasks. Pretraining is an active research topic, encompassing supervised and self-supervised learning methods to initialize model weights effectively. However, transferring the pretrained models to downstream tasks may encounter task discrepancy due to their formulation of pretraining as image classification or object discrimination tasks. In this study, we explore the multitask pretraining (MTP) paradigm for RS foundation models to address this issue. Using a shared encoder and task-specific decoder architecture, we conduct multitask supervised pretraining on the segment anything model annotated remote sensing segmentation dataset, encompassing semantic segmentation, instance segmentation, and rotated object detection. MTP supports both convolutional neural networks and vision transformer foundation models with over 300 million parameters. The pretrained models are finetuned on various RS downstream tasks, such as scene classification, horizontal, and rotated object detection, semantic segmentation, and change detection. Extensive experiments across 14 datasets demonstrate the superiority of our models over existing ones of similar size and their competitive performance compared to larger state-of-the-art models, thus validating the effectiveness of MTP.},
  keywords = {Change detection,Data models,foundation model,Image segmentation,Labeling,multitask pretraining (MTP),Multitasking,object detection,Object detection,remote sensing (RS),scene classification,semantic segmentation,Semantic segmentation,Task analysis},
  file = {C\:\\Users\\steph\\Zotero\\storage\\4GRB2GR3\\Wang et al. - 2024 - MTP Advancing Remote Sensing Foundation Model via.pdf;C\:\\Users\\steph\\Zotero\\storage\\K6RN4D6E\\10547536.html}
}

@inproceedings{wang2024selfexpansion,
  title = {Self-{{Expansion}} of {{Pre-trained Models}} with {{Mixture}} of {{Adapters}} for {{Continual Learning}}},
  booktitle = {{{NeurIPS}} 2024 {{Workshop}} on {{Scalable Continual Learning}} for {{Lifelong Foundation Models}}},
  author = {Wang, Huiyi and Lu, Haodong and Yao, Lina and Gong, Dong},
  year = {2024},
  month = oct,
  urldate = {2024-12-22},
  abstract = {Continual learning (CL) aims to continuously accumulate knowledge from non-stationary data streams without catastrophic forgetting of learned knowledge, requiring a balance between stability and plasticity. Leveraging generalizable representation in pre-trained models (PTMs), PTM-based CL methods adapt effectively to downstream tasks by adding learnable adapters or prompts to frozen PTMs. However, many existing methods restrict adaptation to a fixed set of modules, limiting CL capabilities. Periodically adding task-specific modules leads to linear model growth and impaired knowledge reuse. We propose **S**elf-**E**xpansion of PTMs with **M**odularized **A**daptation (SEMA), a novel approach that enhances stability-plasticity balance by automatically determining when to reuse or add adapter modules depending on if distribution shifts that cannot be handled is detected at different representation levels. Our modular adapter consists of a functional adapter and a representation descriptor, which acts as a distribution shift indicator, triggering self-expansion. An expandable weighting router is learned jointly for mixture of adapter outputs. SEMA enables better knowledge reuse and sub-linear expansion rate. Extensive experiments show SEMA achieves state-of-the-art performance, outperforming PTM-based CL methods without memory rehearsal.},
  langid = {english},
  file = {C:\Users\steph\Zotero\storage\WPM66DA9\Wang et al. - 2024 - Self-Expansion of Pre-trained Models with Mixture of Adapters for Continual Learning.pdf}
}

@article{wang2024transformers,
  title = {Transformers for {{Remote Sensing}}: {{A Systematic Review}} and {{Analysis}}},
  shorttitle = {Transformers for {{Remote Sensing}}},
  author = {Wang, Ruikun and Ma, Lei and He, Guangjun and Johnson, Brian Alan and Yan, Ziyun and Chang, Ming and Liang, Ying},
  year = {2024},
  month = jan,
  journal = {Sensors},
  volume = {24},
  number = {11},
  pages = {3495},
  publisher = {Multidisciplinary Digital Publishing Institute},
  issn = {1424-8220},
  doi = {10.3390/s24113495},
  urldate = {2024-07-24},
  abstract = {Research on transformers in remote sensing (RS), which started to increase after 2021, is facing the problem of a relative lack of review. To understand the trends of transformers in RS, we undertook a quantitative analysis of the major research on transformers over the past two years by dividing the application of transformers into eight domains: land use/land cover (LULC) classification, segmentation, fusion, change detection, object detection, object recognition, registration, and others. Quantitative results show that transformers achieve a higher accuracy in LULC classification and fusion, with more stable performance in segmentation and object detection. Combining the analysis results on LULC classification and segmentation, we have found that transformers need more parameters than convolutional neural networks (CNNs). Additionally, further research is also needed regarding inference speed to improve transformers' performance. It was determined that the most common application scenes for transformers in our database are urban, farmland, and water bodies. We also found that transformers are employed in the natural sciences such as agriculture and environmental protection rather than the humanities or economics. Finally, this work summarizes the analysis results of transformers in remote sensing obtained during the research process and provides a perspective on future directions of development.},
  copyright = {http://creativecommons.org/licenses/by/3.0/},
  langid = {english},
  keywords = {change detection,classification,convolutional neural network,deep learning,image fusion,object detection,recurrent neural networks (RNNs),segmentation,time series},
  file = {C:\Users\steph\Zotero\storage\79ZCWULR\Wang et al. - 2024 - Transformers for Remote Sensing A Systematic Revi.pdf}
}

@article{wang2025comprehensive,
  title = {A {{Comprehensive Survey}} of {{Forgetting}} in {{Deep Learning Beyond Continual Learning}}},
  author = {Wang, Zhenyi and Yang, Enneng and Shen, Li and Huang, Heng},
  year = {2025},
  month = mar,
  journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
  volume = {47},
  number = {3},
  pages = {1464--1483},
  issn = {1939-3539},
  doi = {10.1109/TPAMI.2024.3498346},
  urldate = {2025-03-08},
  abstract = {Forgetting refers to the loss or deterioration of previously acquired knowledge. While existing surveys on forgetting have primarily focused on continual learning, forgetting is a prevalent phenomenon observed in various other research domains within deep learning. Forgetting manifests in research fields such as generative models due to generator shifts, and federated learning due to heterogeneous data distributions across clients. Addressing forgetting encompasses several challenges, including balancing the retention of old task knowledge with fast learning of new task, managing task interference with conflicting goals, and preventing privacy leakage, etc. Moreover, most existing surveys on continual learning implicitly assume that forgetting is always harmful. In contrast, our survey argues that forgetting is a double-edged sword and can be beneficial and desirable in certain cases, such as privacy-preserving scenarios. By exploring forgetting in a broader context, we present a more nuanced understanding of this phenomenon and highlight its potential advantages. Through this comprehensive survey, we aspire to uncover potential solutions by drawing upon ideas and approaches from various fields that have dealt with forgetting. By examining forgetting beyond its conventional boundaries, we hope to encourage the development of novel strategies for mitigating, harnessing, or even embracing forgetting in real applications.},
  keywords = {Adaptation models,Beneficial forgetting,Continuing education,cross-disciplinary research,Data models,Data privacy,distribution shift,Federated learning,harmful forgetting,memorization,Metalearning,Reinforcement learning,Surveys,Training,Training data},
  file = {C:\Users\steph\Zotero\storage\2UX82PXI\10752992.html}
}

@inproceedings{wenzel2022assaying,
  title = {Assaying {{Out-Of-Distribution Generalization}} in {{Transfer Learning}}},
  booktitle = {Advances in {{Neural Information Processing Systems}}},
  author = {Wenzel, Florian and Dittadi, Andrea and Gehler, Peter Vincent and {Simon-Gabriel}, Carl-Johann and Horn, Max and Zietlow, Dominik and Kernert, David and Russell, Chris and Brox, Thomas and Schiele, Bernt and Sch{\"o}lkopf, Bernhard and Locatello, Francesco},
  year = {2022},
  month = oct,
  urldate = {2022-12-05},
  abstract = {Since out-of-distribution generalization is a generally ill-posed problem, various proxy targets (e.g., calibration, adversarial robustness, algorithmic corruptions, invariance across shifts) were studied across different research programs resulting in different recommendations. While sharing the same aspirational goal, these approaches have never been tested under the same experimental conditions on real data. In this paper, we take a unified view of previous work, highlighting message discrepancies that we address empirically, and providing recommendations on how to measure the robustness of a model and how to improve it. To this end, we collect 172 publicly available dataset pairs for training and out-of-distribution evaluation of accuracy, calibration error, adversarial attacks, environment invariance, and synthetic corruptions. We fine-tune over 31k networks, from nine different architectures in the many- and few-shot setting. Our findings confirm that in- and out-of-distribution accuracies tend to increase jointly, but show that their relation is largely dataset-dependent, and in general more nuanced and more complex than posited by previous, smaller scale studies.},
  langid = {english},
  file = {C\:\\Users\\steph\\Zotero\\storage\\9P82V4PC\\Wenzel et al. - 2022 - Assaying Out-Of-Distribution Generalization in Tra.pdf;C\:\\Users\\steph\\Zotero\\storage\\IS2JBUGD\\Wenzel et al. - 2022 - Assaying Out-Of-Distribution Generalization in Tra.pdf;C\:\\Users\\steph\\Zotero\\storage\\KDXP446K\\forum.html}
}

@inproceedings{wiles2022finegrained,
  title = {A {{Fine-Grained Analysis}} on {{Distribution Shift}}},
  booktitle = {International {{Conference}} on {{Learning Representations}}},
  author = {Wiles, Olivia and Gowal, Sven and Stimberg, Florian and Rebuffi, Sylvestre-Alvise and Ktena, Ira and Dvijotham, Krishnamurthy Dj and Cemgil, Ali Taylan},
  year = {2022},
  month = mar,
  urldate = {2022-12-20},
  abstract = {Robustness to distribution shifts is critical for deploying machine learning models in the real world. Despite this necessity, there has been little work in defining the underlying mechanisms that cause these shifts and evaluating the robustness of algorithms across multiple, different distribution shifts. To this end, we introduce a framework that enables fine-grained analysis of various distribution shifts. We provide a holistic analysis of current state-of-the-art methods by evaluating 19 distinct methods grouped into five categories across both synthetic and real-world datasets. Overall, we train more than 85K models. Our experimental framework can be easily extended to include new methods, shifts, and datasets. We find, unlike previous work (Gulrajani \& Lopez-Paz, 2021), that progress has been made over a standard ERM baseline; in particular, pretraining and augmentations (learned or heuristic) offer large gains in many cases. However, the best methods are not consistent over different datasets and shifts. We will open source our experimental framework, allowing future work to evaluate new methods over multiple shifts to obtain a more complete picture of a method's effectiveness. Code is available at github.com/deepmind/distribution\_shift\_framework.},
  langid = {english},
  file = {C:\Users\steph\Zotero\storage\MCYQQGAL\Wiles et al. - 2022 - A Fine-Grained Analysis on Distribution Shift.pdf}
}

@article{xie2021segformer,
  title = {{{SegFormer}}: {{Simple}} and {{Efficient Design}} for {{Semantic Segmentation}} with {{Transformers}}},
  shorttitle = {{{SegFormer}}},
  author = {Xie, Enze and Wang, Wenhai and Yu, Zhiding and Anandkumar, Anima and Alvarez, Jose M. and Luo, Ping},
  year = {2021},
  month = oct,
  journal = {arXiv:2105.15203 [cs]},
  eprint = {2105.15203},
  primaryclass = {cs},
  urldate = {2022-01-06},
  abstract = {We present SegFormer, a simple, efficient yet powerful semantic segmentation framework which unifies Transformers with lightweight multilayer perception (MLP) decoders. SegFormer has two appealing features: 1) SegFormer comprises a novel hierarchically structured Transformer encoder which outputs multiscale features. It does not need positional encoding, thereby avoiding the interpolation of positional codes which leads to decreased performance when the testing resolution differs from training. 2) SegFormer avoids complex decoders. The proposed MLP decoder aggregates information from different layers, and thus combining both local attention and global attention to render powerful representations. We show that this simple and lightweight design is the key to efficient segmentation on Transformers. We scale our approach up to obtain a series of models from SegFormer-B0 to SegFormer-B5, reaching significantly better performance and efficiency than previous counterparts. For example, SegFormer-B4 achieves 50.3\% mIoU on ADE20K with 64M parameters, being 5x smaller and 2.2\% better than the previous best method. Our best model, SegFormer-B5, achieves 84.0\% mIoU on Cityscapes validation set and shows excellent zero-shot robustness on Cityscapes-C. Code will be released at: github.com/NVlabs/SegFormer.},
  archiveprefix = {arXiv},
  keywords = {Computer Science - Computer Vision and Pattern Recognition,Computer Science - Machine Learning},
  file = {C\:\\Users\\steph\\Zotero\\storage\\IVX3WC3M\\Xie et al. - 2021 - SegFormer Simple and Efficient Design for Semanti.pdf;C\:\\Users\\steph\\Zotero\\storage\\EYHHCGX3\\2105.html}
}

@article{xie2024missnet,
  title = {{{MiSSNet}}: {{Memory-Inspired Semantic Segmentation Augmentation Network}} for {{Class-Incremental Learning}} in {{Remote Sensing Images}}},
  shorttitle = {{{MiSSNet}}},
  author = {Xie, Jiajun and Pan, Bin and Xu, Xia and Shi, Zhenwei},
  year = {2024},
  journal = {IEEE Transactions on Geoscience and Remote Sensing},
  volume = {62},
  pages = {1--13},
  issn = {1558-0644},
  doi = {10.1109/TGRS.2024.3360701},
  urldate = {2024-06-19},
  abstract = {With remote sensing images constantly being collected rapidly, the class-incremental semantic segmentation (CISS) task has attracted increasing attention. However, the semantic distribution shift problem of the background class in remote sensing images, which is a case of catastrophic forgetting, continues to limit available CISS algorithms. To address this challenge, we present a new memory-inspired semantic segmentation augmentation network (MiSSNet) for class-incremental learning in remote sensing images. The MiSSNet mainly includes two modules: the local semantic distillation (LSD) module and the class-specific regularization (CSR) module. LSD is a distillation structure that employs the local semantic features in retained memory to maintain correlation between pixels throughout the training process of incremental learning. It constructs a series of pixel-level correlation matrices and implicitly adjusts the semantic distribution shift problem of the background class. CSR is a classwise regularization term that utilizes the class-specific portion of the preserved memory to help the model keep repeating the learning of the old categories. It alleviates the background class shift problem by generating countless pixel-level instances of old classes. LSD and CSR work together to tackle the semantic distribution shift problem of background class from semantic information and class information aspects, respectively. In particular, MiSSNet only needs an additional single inference process for memory extraction and storage, and the whole algorithm does not add any new training parameters. Experimental results on three semantic segmentation datasets indicate the advantage of the proposed method.},
  keywords = {Correlation,Feature extraction,Incremental learning,remote sensing images,semantic segmentation,Semantic segmentation,Semantics,Sensors,Task analysis,Training},
  file = {C:\Users\steph\Zotero\storage\XSG8RVC3\Xie et al. - 2024 - MiSSNet Memory-Inspired Semantic Segmentation Aug.pdf}
}

@inproceedings{xie2025early,
  title = {Early {{Preparation Pays Off}}: {{New Classifier Pre-tuning}} for~{{Class Incremental Semantic Segmentation}}},
  shorttitle = {Early {{Preparation Pays Off}}},
  booktitle = {Computer {{Vision}} -- {{ECCV}} 2024},
  author = {Xie, Zhengyuan and Lu, Haiquan and Xiao, Jia-wen and Wang, Enguang and Zhang, Le and Liu, Xialei},
  editor = {Leonardis, Ale{\v s} and Ricci, Elisa and Roth, Stefan and Russakovsky, Olga and Sattler, Torsten and Varol, G{\"u}l},
  year = {2025},
  pages = {183--201},
  publisher = {Springer Nature Switzerland},
  address = {Cham},
  doi = {10.1007/978-3-031-73347-5_11},
  abstract = {Class incremental semantic segmentation aims to preserve old knowledge while learning new tasks, however, it is impeded by catastrophic forgetting and background shift issues. Prior works indicate the pivotal importance of initializing new classifiers and mainly focus on transferring knowledge from the background classifier or preparing classifiers for future classes, neglecting the flexibility and variance of new classifiers. In this paper, we propose a new classifier pre-tuning~(NeST) method applied before the formal training process, learning a transformation from old classifiers to generate new classifiers for initialization rather than directly tuning the parameters of new classifiers. Our method can make new classifiers align with the backbone and adapt to the new data, preventing drastic changes in the feature extractor when learning new classes. Besides, we design a strategy considering the cross-task class similarity to initialize matrices used in the transformation, helping achieve the stability-plasticity trade-off. Experiments on Pascal VOC 2012 and ADE20K datasets show that the proposed strategy can significantly improve the performance of previous methods. The code is available at https://github.com/zhengyuan-xie/ECCV24\_NeST.},
  isbn = {978-3-031-73347-5},
  langid = {english}
}

@inproceedings{xinrui2024forgetting,
  title = {Forgetting, {{Ignorance}} or {{Myopia}}: {{Revisiting Key Challenges}} in {{Online Continual Learning}}},
  shorttitle = {Forgetting, {{Ignorance}} or {{Myopia}}},
  booktitle = {The {{Thirty-eighth Annual Conference}} on {{Neural Information Processing Systems}}},
  author = {Xinrui, Wang and Geng, Chuanxing and Wan, Wenhai and Li, Shao-Yuan and Chen, Songcan},
  year = {2024},
  month = nov,
  urldate = {2025-03-09},
  abstract = {Online continual learning (OCL) requires the models to learn from constant, endless streams of data. While significant efforts have been made in this field, most were focused on mitigating the {\textbackslash}textit\{catastrophic forgetting\} issue to achieve better classification ability, at the cost of a much heavier training workload. They overlooked that in real-world scenarios, e.g., in high-speed data stream environments, data do not pause to accommodate slow models. In this paper, we emphasize that {\textbackslash}textit\{model throughput\}-- defined as the maximum number of training samples that a model can process within a unit of time -- is equally important. It directly limits how much data a model can utilize and presents a challenging dilemma for current methods. With this understanding, we revisit key challenges in OCL from both empirical and theoretical perspectives, highlighting two critical issues beyond the well-documented catastrophic forgetting: ({\textbackslash}romannumeral1) Model's ignorance: the single-pass nature of OCL challenges models to learn effective features within constrained training time and storage capacity, leading to a trade-off between effective learning and model throughput; ({\textbackslash}romannumeral2) Model's myopia: the local learning nature of OCL on the current task leads the model to adopt overly simplified, task-specific features and {\textbackslash}textit\{excessively sparse classifier\}, resulting in the gap between the optimal solution for the current task and the global objective. To tackle these issues, we propose the Non-sparse Classifier Evolution framework (NsCE) to facilitate effective global discriminative feature learning with minimal time cost. NsCE integrates non-sparse maximum separation regularization and targeted experience replay techniques with the help of pre-trained models, enabling rapid acquisition of new globally discriminative features. Extensive experiments demonstrate the substantial improvements of our framework in performance, throughput and real-world practicality.},
  langid = {english},
  file = {C:\Users\steph\Zotero\storage\8JE8QUBR\Xinrui et al. - 2024 - Forgetting, Ignorance or Myopia Revisiting Key Challenges in Online Continual Learning.pdf}
}

@misc{xiong2024neural,
  title = {Neural {{Plasticity-Inspired Multimodal Foundation Model}} for {{Earth Observation}}},
  author = {Xiong, Zhitong and Wang, Yi and Zhang, Fahong and Stewart, Adam J. and Hanna, Jo{\"e}lle and Borth, Damian and Papoutsis, Ioannis and Saux, Bertrand Le and {Camps-Valls}, Gustau and Zhu, Xiao Xiang},
  year = {2024},
  month = jun,
  number = {arXiv:2403.15356},
  eprint = {2403.15356},
  primaryclass = {cs},
  publisher = {arXiv},
  doi = {10.48550/arXiv.2403.15356},
  urldate = {2024-07-24},
  abstract = {The development of foundation models has revolutionized our ability to interpret the Earth's surface using satellite observational data. Traditional models have been siloed, tailored to specific sensors or data types like optical, radar, and hyperspectral, each with its own unique characteristics. This specialization hinders the potential for a holistic analysis that could benefit from the combined strengths of these diverse data sources. Our novel approach introduces the Dynamic One-For-All (DOFA) model, leveraging the concept of neural plasticity in brain science to integrate various data modalities into a single framework adaptively. This dynamic hypernetwork, adjusting to different wavelengths, enables a single versatile Transformer jointly trained on data from five sensors to excel across 12 distinct Earth observation tasks, including sensors never seen during pretraining. DOFA's innovative design offers a promising leap towards more accurate, efficient, and unified Earth observation analysis, showcasing remarkable adaptability and performance in harnessing the potential of multimodal Earth observation data.},
  archiveprefix = {arXiv},
  keywords = {Computer Science - Computer Vision and Pattern Recognition},
  file = {C\:\\Users\\steph\\Zotero\\storage\\WBCPK72Q\\Xiong et al. - 2024 - Neural Plasticity-Inspired Multimodal Foundation M.pdf;C\:\\Users\\steph\\Zotero\\storage\\49K93ZLK\\2403.html}
}

@article{xu2021improved,
  title = {An {{Improved Swin Transformer-Based Model}} for {{Remote Sensing Object Detection}} and {{Instance Segmentation}}},
  author = {Xu, Xiangkai and Feng, Zhejun and Cao, Changqing and Li, Mengyuan and Wu, Jin and Wu, Zengyan and Shang, Yajie and Ye, Shubing},
  year = {2021},
  month = jan,
  journal = {Remote Sensing},
  volume = {13},
  number = {23},
  pages = {4779},
  publisher = {Multidisciplinary Digital Publishing Institute},
  doi = {10.3390/rs13234779},
  urldate = {2022-01-21},
  abstract = {Remote sensing image object detection and instance segmentation are widely valued research fields. A convolutional neural network (CNN) has shown defects in the object detection of remote sensing images. In recent years, the number of studies on transformer-based models increased, and these studies achieved good results. However, transformers still suffer from poor small object detection and unsatisfactory edge detail segmentation. In order to solve these problems, we improved the Swin transformer based on the advantages of transformers and CNNs, and designed a local perception Swin transformer (LPSW) backbone to enhance the local perception of the network and to improve the detection accuracy of small-scale objects. We also designed a spatial attention interleaved execution cascade (SAIEC) network framework, which helped to strengthen the segmentation accuracy of the network. Due to the lack of remote sensing mask datasets, the MRS-1800 remote sensing mask dataset was created. Finally, we combined the proposed backbone with the new network framework and conducted experiments on this MRS-1800 dataset. Compared with the Swin transformer, the proposed model improved the mask AP by 1.7\%, mask APS by 3.6\%, AP by 1.1\% and APS by 4.6\%, demonstrating its effectiveness and feasibility.},
  copyright = {http://creativecommons.org/licenses/by/3.0/},
  langid = {english},
  keywords = {cascade mask R-CNN,instance segmentation,object detection,remote sensing,remote sensing image,Swin transformer,transformer},
  file = {C\:\\Users\\steph\\Zotero\\storage\\P2T24LVP\\Xu et al. - 2021 - An Improved Swin Transformer-Based Model for Remot.pdf;C\:\\Users\\steph\\Zotero\\storage\\PFKCLEP4\\4779.html}
}

@misc{xu2022delving,
  title = {Delving into {{Transformer}} for {{Incremental Semantic Segmentation}}},
  author = {Xu, Zekai and Zhang, Mingyi and Hou, Jiayue and Gong, Xing and Wen, Chuan and Wang, Chengjie and Zhang, Junge},
  year = {2022},
  month = nov,
  number = {arXiv:2211.10253},
  eprint = {2211.10253},
  primaryclass = {cs},
  publisher = {arXiv},
  doi = {10.48550/arXiv.2211.10253},
  urldate = {2023-02-23},
  abstract = {Incremental semantic segmentation(ISS) is an emerging task where old model is updated by incrementally adding new classes. At present, methods based on convolutional neural networks are dominant in ISS. However, studies have shown that such methods have difficulty in learning new tasks while maintaining good performance on old ones (catastrophic forgetting). In contrast, a Transformer based method has a natural advantage in curbing catastrophic forgetting due to its ability to model both long-term and short-term tasks. In this work, we explore the reasons why Transformer based architecture are more suitable for ISS, and accordingly propose propose TISS, a Transformer based method for Incremental Semantic Segmentation. In addition, to better alleviate catastrophic forgetting while preserving transferability on ISS, we introduce two patch-wise contrastive losses to imitate similar features and enhance feature diversity respectively, which can further improve the performance of TISS. Under extensive experimental settings with Pascal-VOC 2012 and ADE20K datasets, our method significantly outperforms state-of-the-art incremental semantic segmentation methods.},
  archiveprefix = {arXiv},
  keywords = {Computer Science - Computer Vision and Pattern Recognition},
  file = {C\:\\Users\\steph\\Zotero\\storage\\HDG443XK\\Xu et al. - 2022 - Delving into Transformer for Incremental Semantic .pdf;C\:\\Users\\steph\\Zotero\\storage\\RMBYXIT6\\2211.html}
}

@inproceedings{yang2022explaining,
  title = {Explaining {{Deep Convolutional Neural Networks}} via {{Latent Visual-Semantic Filter Attention}}},
  booktitle = {Proceedings of the {{IEEE}}/{{CVF Conference}} on {{Computer Vision}} and {{Pattern Recognition}}},
  author = {Yang, Yu and Kim, Seungbae and Joo, Jungseock},
  year = {2022},
  pages = {8333--8343},
  urldate = {2023-01-09},
  langid = {english},
  file = {C:\Users\steph\Zotero\storage\JMQ2XJEW\Yang et al. - 2022 - Explaining Deep Convolutional Neural Networks via .pdf}
}

@article{yang2022inducing,
  title = {Inducing {{Neural Collapse}} in {{Imbalanced Learning}}: {{Do We Really Need}} a {{Learnable Classifier}} at the {{End}} of {{Deep Neural Network}}?},
  shorttitle = {Inducing {{Neural Collapse}} in {{Imbalanced Learning}}},
  author = {Yang, Yibo and Chen, Shixiang and Li, Xiangtai and Xie, Liang and Lin, Zhouchen and Tao, Dacheng},
  year = {2022},
  month = dec,
  journal = {Advances in Neural Information Processing Systems},
  volume = {35},
  pages = {37991--38002},
  urldate = {2025-01-05},
  langid = {english},
  file = {C:\Users\steph\Zotero\storage\Z4UUBPVB\Yang et al. - 2022 - Inducing Neural Collapse in Imbalanced Learning Do We Really Need a Learnable Classifier at the End.pdf}
}

@inproceedings{yosinski2014how,
  title = {How Transferable Are Features in Deep Neural Networks?},
  booktitle = {Advances in Neural Information Processing Systems},
  author = {Yosinski, Jason and Clune, Jeff and Bengio, Yoshua and Lipson, Hod},
  year = {2014},
  pages = {3320--3328}
}

@misc{yu2023foundation,
  title = {Foundation {{Model Drives Weakly Incremental Learning}} for {{Semantic Segmentation}}},
  author = {Yu, Chaohui and Zhou, Qiang and Li, Jingliang and Yuan, Jianlong and Wang, Zhibin and Wang, Fan},
  year = {2023},
  month = feb,
  number = {arXiv:2302.14250},
  eprint = {2302.14250},
  primaryclass = {cs},
  publisher = {arXiv},
  doi = {10.48550/arXiv.2302.14250},
  urldate = {2023-03-02},
  abstract = {Modern incremental learning for semantic segmentation methods usually learn new categories based on dense annotations. Although achieve promising results, pixel-by-pixel labeling is costly and time-consuming. Weakly incremental learning for semantic segmentation (WILSS) is a novel and attractive task, which aims at learning to segment new classes from cheap and widely available image-level labels. Despite the comparable results, the image-level labels can not provide details to locate each segment, which limits the performance of WILSS. This inspires us to think how to improve and effectively utilize the supervision of new classes given image-level labels while avoiding forgetting old ones. In this work, we propose a novel and data-efficient framework for WILSS, named FMWISS. Specifically, we propose pre-training based co-segmentation to distill the knowledge of complementary foundation models for generating dense pseudo labels. We further optimize the noisy pseudo masks with a teacher-student architecture, where a plug-in teacher is optimized with a proposed dense contrastive loss. Moreover, we introduce memory-based copy-paste augmentation to improve the catastrophic forgetting problem of old classes. Extensive experiments on Pascal VOC and COCO datasets demonstrate the superior performance of our framework, e.g., FMWISS achieves 70.7\% and 73.3\% in the 15-5 VOC setting, outperforming the state-of-the-art method by 3.4\% and 6.1\%, respectively.},
  archiveprefix = {arXiv},
  keywords = {Computer Science - Computer Vision and Pattern Recognition},
  file = {C\:\\Users\\steph\\Zotero\\storage\\C6WMUN5E\\Yu et al. - 2023 - Foundation Model Drives Weakly Incremental Learnin.pdf;C\:\\Users\\steph\\Zotero\\storage\\YX4BKDTG\\2302.html}
}

@article{yuan2024survey,
  title = {A {{Survey}} on {{Continual Semantic Segmentation}}: {{Theory}}, {{Challenge}}, {{Method}} and {{Application}}},
  shorttitle = {A {{Survey}} on {{Continual Semantic Segmentation}}},
  author = {Yuan, Bo and Zhao, Danpei},
  year = {2024},
  month = dec,
  journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
  volume = {46},
  number = {12},
  pages = {10891--10910},
  issn = {1939-3539},
  doi = {10.1109/TPAMI.2024.3446949},
  urldate = {2025-03-09},
  abstract = {Continual learning, also known as incremental learning or life-long learning, stands at the forefront of deep learning and AI systems. It breaks through the obstacle of one-way training on close sets and enables continuous adaptive learning on open-set conditions. In the recent decade, continual learning has been explored and applied in multiple fields especially in computer vision covering classification, detection and segmentation tasks. Continual semantic segmentation (CSS), of which the dense prediction peculiarity makes it a challenging, intricate and burgeoning task. In this paper, we present a review of CSS, committing to building a comprehensive survey on problem formulations, primary challenges, universal datasets, neoteric theories and multifarious applications. Concretely, we begin by elucidating the problem definitions and primary challenges. Based on an in-depth investigation of relevant approaches, we sort out and categorize current CSS models into two main branches including data-replay and data-free sets. In each branch, the corresponding approaches are similarity-based clustered and thoroughly analyzed, following qualitative comparison and quantitative reproductions on relevant datasets. Besides, we also introduce four CSS specialities with diverse application scenarios and development tendencies. Furthermore, we develop a benchmark for CSS encompassing representative references, evaluation results and reproductions. We hope this survey can serve as a reference-worthy and stimulating contribution to the advancement of the life-long learning field, while also providing valuable perspectives for related fields.},
  keywords = {catastrophic forgetting,Continual semantic segmentation,Data models,incremental learning,life-long learning,Predictive models,Presence network agents,semantic drift,Semantic segmentation,Semantics,Surveys,Task analysis},
  file = {C:\Users\steph\Zotero\storage\QWDLNJXQ\Yuan et Zhao - 2024 - A Survey on Continual Semantic Segmentation Theory, Challenge, Method and Application.pdf}
}

@article{zhang2024every,
  title = {Behind {{Every Domain There}} Is a {{Shift}}: {{Adapting Distortion-aware Vision Transformers}} for {{Panoramic Semantic Segmentation}}},
  shorttitle = {Behind {{Every Domain There}} Is a {{Shift}}},
  author = {Zhang, Jiaming and Yang, Kailun and Shi, Hao and Rei{\ss}, Simon and Peng, Kunyu and Ma, Chaoxiang and Fu, Haodong and Torr, Philip H. S. and Wang, Kaiwei and Stiefelhagen, Rainer},
  year = {2024},
  journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
  pages = {1--17},
  issn = {1939-3539},
  doi = {10.1109/TPAMI.2024.3408642},
  urldate = {2024-09-16},
  abstract = {In this paper, we address panoramic semantic segmentation which is under-explored due to two critical challenges: (1) image distortions and object deformations on panoramas; (2) lack of semantic annotations in the 360{\textasciicircum}{\textbackslash}circ imagery. To tackle these problems, first, we propose the upgraded Transformer for Panoramic Semantic Segmentation, ie, Trans4PASS+, equipped with Deformable Patch Embedding (DPE) and Deformable MLP (DMLPv2) modules for handling object deformations and image distortions whenever (before or after adaptation) and wherever (shallow or deep levels). Second, we enhance the Mutual Prototypical Adaptation (MPA) strategy via pseudo-label rectification for unsupervised domain adaptive panoramic segmentation. Third, aside from Pinhole-to-Panoramic (Pin2Pan) adaptation, we create a new dataset (SynPASS) with 9,080 panoramic images, facilitating Synthetic-to-Real (Syn2Real) adaptation scheme in 360{\textasciicircum}{\textbackslash}circ imagery. Extensive experiments are conducted, which cover indoor and outdoor scenarios, and each of them is investigated with Pin2Pan and Syn2Real regimens. Trans4PASS+ achieves state-of-the-art performances on four domain adaptive panoramic semantic segmentation benchmarks. Code is available at https://github.com/jamycheung/Trans4PASS.},
  keywords = {Adaptation models,Deformation,Domain Adaptation,Panoramic Images,Scene Understanding,Semantic segmentation,Semantic Segmentation,Semantics,Solid modeling,Task analysis,Transformers,Vision Transformers},
  file = {C\:\\Users\\steph\\Zotero\\storage\\MXQCZF7D\\Zhang et al. - 2024 - Behind Every Domain There is a Shift Adapting Dis.pdf;C\:\\Users\\steph\\Zotero\\storage\\D4SHG952\\10546335.html}
}

@article{zhang2024segvit,
  title = {{{SegViT}} v2: {{Exploring Efficient}} and {{Continual Semantic Segmentation}} with {{Plain Vision Transformers}}},
  shorttitle = {{{SegViT}} V2},
  author = {Zhang, Bowen and Liu, Liyang and Phan, Minh Hieu and Tian, Zhi and Shen, Chunhua and Liu, Yifan},
  year = {2024},
  month = apr,
  journal = {International Journal of Computer Vision},
  volume = {132},
  number = {4},
  pages = {1126--1147},
  issn = {1573-1405},
  doi = {10.1007/s11263-023-01894-8},
  urldate = {2024-06-19},
  abstract = {This paper investigates the capability of plain Vision Transformers (ViTs) for semantic segmentation using the encoder--decoder framework and introduce SegViTv2. In this study, we introduce a novel Attention-to-Mask (ATM) module to design a lightweight decoder effective for plain ViT. The proposed ATM converts the global attention map into semantic masks for high-quality segmentation results. Our decoder outperforms popular decoder UPerNet using various ViT backbones while consuming only about \$\$5{\textbackslash}\%\$\$of the computational cost. For the encoder, we address the concern of the relatively high computational cost in the ViT-based encoders and propose a Shrunk++ structure that incorporates edge-aware query-based down-sampling (EQD) and query-based up-sampling (QU) modules. The Shrunk++ structure reduces the computational cost of the encoder by up to \$\$50{\textbackslash}\%\$\$while maintaining competitive performance. Furthermore, we propose to adapt SegViT for continual semantic segmentation, demonstrating nearly zero forgetting of previously learned knowledge. Experiments show that our proposed SegViTv2 surpasses recent segmentation methods on three popular benchmarks including ADE20k, COCO-Stuff-10k and PASCAL-Context datasets. The code is available through the following link: https://github.com/zbwxp/SegVit.},
  langid = {english},
  keywords = {Continual learning,Incremental learning,Semantic segmentation,Vision transformer},
  file = {C:\Users\steph\Zotero\storage\9FHSKK9T\Zhang et al. - 2024 - SegViT v2 Exploring Efficient and Continual Seman.pdf}
}

@misc{zhang2024urban,
  title = {Towards {{Urban General Intelligence}}: {{A Review}} and {{Outlook}} of {{Urban Foundation Models}}},
  shorttitle = {Towards {{Urban General Intelligence}}},
  author = {Zhang, Weijia and Han, Jindong and Xu, Zhao and Ni, Hang and Liu, Hao and Xiong, Hui},
  year = {2024},
  month = jan,
  number = {arXiv:2402.01749},
  eprint = {2402.01749},
  primaryclass = {cs},
  publisher = {arXiv},
  doi = {10.48550/arXiv.2402.01749},
  urldate = {2024-07-24},
  abstract = {Machine learning techniques are now integral to the advancement of intelligent urban services, playing a crucial role in elevating the efficiency, sustainability, and livability of urban environments. The recent emergence of foundation models such as ChatGPT marks a revolutionary shift in the fields of machine learning and artificial intelligence. Their unparalleled capabilities in contextual understanding, problem solving, and adaptability across a wide range of tasks suggest that integrating these models into urban domains could have a transformative impact on the development of smart cities. Despite growing interest in Urban Foundation Models{\textasciitilde}(UFMs), this burgeoning field faces challenges such as a lack of clear definitions, systematic reviews, and universalizable solutions. To this end, this paper first introduces the concept of UFM and discusses the unique challenges involved in building them. We then propose a data-centric taxonomy that categorizes current UFM-related works, based on urban data modalities and types. Furthermore, to foster advancement in this field, we present a promising framework aimed at the prospective realization of UFMs, designed to overcome the identified challenges. Additionally, we explore the application landscape of UFMs, detailing their potential impact in various urban contexts. Relevant papers and open-source resources have been collated and are continuously updated at https://github.com/usail-hkust/Awesome-Urban-Foundation-Models.},
  archiveprefix = {arXiv},
  keywords = {Computer Science - Artificial Intelligence,Computer Science - Computers and Society,Computer Science - Machine Learning},
  file = {C\:\\Users\\steph\\Zotero\\storage\\J6JUYCM3\\Zhang et al. - 2024 - Towards Urban General Intelligence A Review and O.pdf;C\:\\Users\\steph\\Zotero\\storage\\X7GRJUF7\\2402.html}
}

@misc{zhang2024when,
  title = {When {{Geoscience Meets Foundation Models}}: {{Towards General Geoscience Artificial Intelligence System}}},
  shorttitle = {When {{Geoscience Meets Foundation Models}}},
  author = {Zhang, Hao and Xu, Jin-Jian and Cui, Hong-Wei and Li, Lin and Yang, Yaowen and Tang, Chao-Sheng and Boers, Niklas},
  year = {2024},
  month = mar,
  number = {arXiv:2309.06799},
  eprint = {2309.06799},
  primaryclass = {physics},
  publisher = {arXiv},
  doi = {10.48550/arXiv.2309.06799},
  urldate = {2024-07-24},
  abstract = {Geoscience foundation models (GFMs) represent a revolutionary approach within Earth sciences to integrate massive cross-disciplinary data for improved simulation and understanding of Earth system dynamics. As a data-centric artificial intelligence paradigm, GFMs extract valuable insights from petabytes of both structured and unstructured data. Their versatility in task specification, diverse inputs and outputs, and multi-modal knowledge representation enable a comprehensive analysis that surpasses the capabilities of individual data sources. Critically, the scalability and generalizability of GFMs empower them to address a wide array of prediction, simulation, and decision tasks related to the intricate interactions among Earth system components. By unraveling the causal mechanisms underlying observed patterns and changes, GFMs contribute to advancing our knowledge of the Earth system and its responses to various drivers and perturbations. Collaboration between domain experts and computer scientists plays a pivotal role in fostering innovations in these invaluable tools for understanding the past, present, and future of our planet. Moreover, we introduce recent advances including key technologies for constructing GFMs, especially remote sensing applications. However, challenges remain in validation and verification, scalability, interpretability, knowledge representation, and addressing social bias. Going forward, the key lies in enhancing model integration, resolution, accuracy, and equity through interdisciplinary teamwork. Despite current limitations, GFMs hold great promise for providing critical insights into pressing issues including climate change, natural hazards, and sustainability through their ability to explore multiple scenarios and quantify uncertainties. Their continued evolution toward integrated, data-driven modeling holds paradigm-shifting potential for Earth science.},
  archiveprefix = {arXiv},
  keywords = {Computer Science - Artificial Intelligence,Physics - Geophysics},
  file = {C\:\\Users\\steph\\Zotero\\storage\\GAQNBJSR\\Zhang et al. - 2024 - When Geoscience Meets Foundation Models Towards G.pdf;C\:\\Users\\steph\\Zotero\\storage\\SGHADHSS\\2309.html}
}

@misc{zhou2023comprehensive,
  title = {A {{Comprehensive Survey}} on {{Pretrained Foundation Models}}: {{A History}} from {{BERT}} to {{ChatGPT}}},
  shorttitle = {A {{Comprehensive Survey}} on {{Pretrained Foundation Models}}},
  author = {Zhou, Ce and Li, Qian and Li, Chen and Yu, Jun and Liu, Yixin and Wang, Guangjing and Zhang, Kai and Ji, Cheng and Yan, Qiben and He, Lifang and Peng, Hao and Li, Jianxin and Wu, Jia and Liu, Ziwei and Xie, Pengtao and Xiong, Caiming and Pei, Jian and Yu, Philip S. and Sun, Lichao},
  year = {2023},
  month = may,
  number = {arXiv:2302.09419},
  eprint = {2302.09419},
  primaryclass = {cs},
  publisher = {arXiv},
  doi = {10.48550/arXiv.2302.09419},
  urldate = {2025-01-29},
  abstract = {Pretrained Foundation Models (PFMs) are regarded as the foundation for various downstream tasks with different data modalities. A PFM (e.g., BERT, ChatGPT, and GPT-4) is trained on large-scale data which provides a reasonable parameter initialization for a wide range of downstream applications. BERT learns bidirectional encoder representations from Transformers, which are trained on large datasets as contextual language models. Similarly, the generative pretrained transformer (GPT) method employs Transformers as the feature extractor and is trained using an autoregressive paradigm on large datasets. Recently, ChatGPT shows promising success on large language models, which applies an autoregressive language model with zero shot or few shot prompting. The remarkable achievements of PFM have brought significant breakthroughs to various fields of AI. Numerous studies have proposed different methods, raising the demand for an updated survey. This study provides a comprehensive review of recent research advancements, challenges, and opportunities for PFMs in text, image, graph, as well as other data modalities. The review covers the basic components and existing pretraining methods used in natural language processing, computer vision, and graph learning. Additionally, it explores advanced PFMs used for different data modalities and unified PFMs that consider data quality and quantity. The review also discusses research related to the fundamentals of PFMs, such as model efficiency and compression, security, and privacy. Finally, the study provides key implications, future research directions, challenges, and open problems in the field of PFMs. Overall, this survey aims to shed light on the research of the PFMs on scalability, security, logical reasoning ability, cross-domain learning ability, and the user-friendly interactive ability for artificial general intelligence.},
  archiveprefix = {arXiv},
  keywords = {Computer Science - Artificial Intelligence,Computer Science - Computation and Language,Computer Science - Machine Learning},
  file = {C\:\\Users\\steph\\Zotero\\storage\\WD8HDYCD\\Zhou et al. - 2023 - A Comprehensive Survey on Pretrained Foundation Models A History from BERT to ChatGPT.pdf;C\:\\Users\\steph\\Zotero\\storage\\8HCZ3BW4\\2302.html}
}

@article{zhou2023graph,
  title = {Graph {{Attention Guidance Network With Knowledge Distillation}} for {{Semantic Segmentation}} of {{Remote Sensing Images}}},
  author = {Zhou, Wujie and Fan, Xiaomin and Yan, Weiqing and Shan, Shengdao and Jiang, Qiuping and Hwang, Jenq-Neng},
  year = {2023},
  journal = {IEEE Transactions on Geoscience and Remote Sensing},
  volume = {61},
  pages = {1--15},
  issn = {1558-0644},
  doi = {10.1109/TGRS.2023.3311480},
  urldate = {2024-06-19},
  abstract = {Deep learning has become a popular method for studying the semantic segmentation of high-resolution remote sensing images (HRRSIs). Existing methods have adopted convolutional neural networks (CNNs) to achieve better segmentation accuracy of HRRSIs, and the success of these models often depends on the model complexity and parameter quantity. However, the deployment of these models on equipment with limited resources is a significant challenge. To solve this problem, a lightweight student network framework---a graph attention guidance network (GAGNet) with knowledge distillation (KD), called GAGNet-S*---is proposed in this study, which distills knowledge from pretrained large teacher network (GAGNet-T) and builds reliable weak labels to optimize untrained student network (GAGNet-S). Inspired by the graph convolution network, this study designs a graph convolution module called the attention-graph decoder (AGD), which combines attention mechanisms with graph convolution to optimize image features and improve segmentation accuracy in the semantic segmentation task of HRRSIs. In addition, a dense cross-decoder (DCD) was designed for multiscale dense fusion, which utilizes rich semantic information in the high-level features to guide and refine the low-level features from the bottom up. Extensive experiments showed that GAGNet-S* (GAGNet-S with KD) achieved excellent segmentation performance on two widely used datasets: Potsdam and Vaihingen. The code and models are available at https://github.com/F8AoMn/GAGNet-KD.},
  keywords = {Computational modeling,Decoding,Dense cross-decoder (DCD),Feature extraction,graph convolution,high-resolution remote sensing images (HRRSIs),knowledge distillation (KD),Knowledge engineering,semantic segmentation,Semantic segmentation,Semantics,Task analysis},
  file = {C:\Users\steph\Zotero\storage\HKMRL7XW\Zhou et al. - 2023 - Graph Attention Guidance Network With Knowledge Di.pdf}
}

@inproceedings{zhou2024continual,
  title = {Continual Learning with Pre-Trained Models: A Survey},
  shorttitle = {Continual Learning with Pre-Trained Models},
  booktitle = {Proceedings of the {{Thirty-Third International Joint Conference}} on {{Artificial Intelligence}}},
  author = {Zhou, Da-Wei and Sun, Hai-Long and Ning, Jingyi and Ye, Han-Jia and Zhan, De-Chuan},
  year = {2024},
  month = aug,
  series = {{{IJCAI}} '24},
  pages = {8363--8371},
  address = {Jeju, Korea},
  doi = {10.24963/ijcai.2024/924},
  urldate = {2025-03-08},
  abstract = {Nowadays, real-world applications often face streaming data, which requires the learning system to absorb new knowledge as data evolves. Continual Learning (CL) aims to achieve this goal and meanwhile overcome the catastrophic forgetting of former knowledge when learning new ones. Typical CL methods build the model from scratch to grow with incoming data. However, the advent of the pre-trained model (PTM) era has sparked immense research interest, particularly in leveraging PTMs' robust representational capabilities for CL. This paper presents a comprehensive survey of the latest advancements in PTM-based CL. We categorize existing methodologies into three distinct groups, providing a comparative analysis of their similarities, differences, and respective advantages and disadvantages. Additionally, we offer an empirical study contrasting various state-of-the-art methods to highlight concerns regarding fairness in comparisons. The source code to reproduce these evaluations is available at: https://github.com/sun-hailong/LAMDA-PILOT.},
  isbn = {978-1-956792-04-1},
  file = {C:\Users\steph\Zotero\storage\UKAP4MQB\Zhou et al. - 2024 - Continual learning with pre-trained models a survey.pdf}
}

@misc{zhou2024image,
  title = {Image {{Segmentation}} in {{Foundation Model Era}}: {{A Survey}}},
  shorttitle = {Image {{Segmentation}} in {{Foundation Model Era}}},
  author = {Zhou, Tianfei and Zhang, Fei and Chang, Boyu and Wang, Wenguan and Yuan, Ye and Konukoglu, Ender and Cremers, Daniel},
  year = {2024},
  month = aug,
  number = {arXiv:2408.12957},
  eprint = {2408.12957},
  primaryclass = {cs},
  publisher = {arXiv},
  doi = {10.48550/arXiv.2408.12957},
  urldate = {2024-09-02},
  abstract = {Image segmentation is a long-standing challenge in computer vision, studied continuously over several decades, as evidenced by seminal algorithms such as N-Cut, FCN, and MaskFormer. With the advent of foundation models (FMs), contemporary segmentation methodologies have embarked on a new epoch by either adapting FMs (e.g., CLIP, Stable Diffusion, DINO) for image segmentation or developing dedicated segmentation foundation models (e.g., SAM). These approaches not only deliver superior segmentation performance, but also herald newfound segmentation capabilities previously unseen in deep learning context. However, current research in image segmentation lacks a detailed analysis of distinct characteristics, challenges, and solutions associated with these advancements. This survey seeks to fill this gap by providing a thorough review of cutting-edge research centered around FM-driven image segmentation. We investigate two basic lines of research -- generic image segmentation (i.e., semantic segmentation, instance segmentation, panoptic segmentation), and promptable image segmentation (i.e., interactive segmentation, referring segmentation, few-shot segmentation) -- by delineating their respective task settings, background concepts, and key challenges. Furthermore, we provide insights into the emergence of segmentation knowledge from FMs like CLIP, Stable Diffusion, and DINO. An exhaustive overview of over 300 segmentation approaches is provided to encapsulate the breadth of current research efforts. Subsequently, we engage in a discussion of open issues and potential avenues for future research. We envisage that this fresh, comprehensive, and systematic survey catalyzes the evolution of advanced image segmentation systems.},
  archiveprefix = {arXiv},
  keywords = {Computer Science - Computer Vision and Pattern Recognition},
  file = {C\:\\Users\\steph\\Zotero\\storage\\AYH6EW5S\\Zhou et al. - 2024 - Image Segmentation in Foundation Model Era A Surv.pdf;C\:\\Users\\steph\\Zotero\\storage\\Q6TRRBWL\\2408.html}
}

@article{zhou2024mesam,
  title = {{{MeSAM}}: {{Multiscale Enhanced Segment Anything Model}} for {{Optical Remote Sensing Images}}},
  shorttitle = {{{MeSAM}}},
  author = {Zhou, Xichuan and Liang, Fu and Chen, Lihui and Liu, Haijun and Song, Qianqian and Vivone, Gemine and Chanussot, Jocelyn},
  year = {2024},
  journal = {IEEE Transactions on Geoscience and Remote Sensing},
  volume = {62},
  pages = {1--15},
  issn = {1558-0644},
  doi = {10.1109/TGRS.2024.3398038},
  urldate = {2024-07-24},
  abstract = {Segment anything model (SAM) has been widely applied to various downstream tasks for its excellent performance and generalization capability. However, SAM exhibits three limitations related to remote sensing (RS) semantic segmentation task: 1) the image encoders excessively lose high-frequency information, such as object boundaries and textures, resulting in rough segmentation masks; 2) due to being trained on natural images, SAM faces difficulty in accurately recognizing objects with large-scale variations and uneven distribution in RS images; and 3) the output tokens used for mask prediction are trained on natural images and not applicable to RS image segmentation. In this article, we explore an efficient paradigm for applying SAM to the semantic segmentation of RS images. Furthermore, we propose multiscale enhanced SAM (MeSAM), a new SAM fine-tuning method more suitable for RS images to adapt it to semantic segmentation tasks. Our method first introduces an inception mixer into the image encoder to effectively preserve high-frequency features. Second, by designing a mask decoder with RS correction and incorporating multiscale connections, we make up the difference in SAM from natural images to RS images. Experimental results demonstrated that our method significantly improves the segmentation accuracy of SAM for RS images, outperforming some state-of-the-art (SOTA) methods. The code will be available at https://github.com/Magic-lem/MeSAM.},
  keywords = {Computational modeling,High-frequency,multiscale,Remote sensing,remote sensing (RS),segment anything model (SAM),semantic segmentation,Semantic segmentation,Semantics,Task analysis,Transformers,Visualization},
  file = {C\:\\Users\\steph\\Zotero\\storage\\2XHQCT7A\\Zhou et al. - 2024 - MeSAM Multiscale Enhanced Segment Anything Model .pdf;C\:\\Users\\steph\\Zotero\\storage\\3AQA4X8U\\10522788.html}
}

@inproceedings{zhou2024unlocking,
  title = {Unlocking the {{Potential}} of {{Pre-trained Vision Transformers}} for {{Few-Shot Semantic Segmentation}} through {{Relationship Descriptors}}},
  booktitle = {Proceedings of the {{IEEE}}/{{CVF Conference}} on {{Computer Vision}} and {{Pattern Recognition}}},
  author = {Zhou, Ziqin and Xu, Hai-Ming and Shu, Yangyang and Liu, Lingqiao},
  year = {2024},
  pages = {3817--3827},
  urldate = {2024-09-18},
  langid = {english},
  file = {C:\Users\steph\Zotero\storage\DGUUU74K\Zhou et al. - 2024 - Unlocking the Potential of Pre-trained Vision Tran.pdf}
}

@article{zhou2025revisiting,
  title = {Revisiting {{Class-Incremental Learning}} with {{Pre-Trained Models}}: {{Generalizability}} and {{Adaptivity}} Are {{All You Need}}},
  shorttitle = {Revisiting {{Class-Incremental Learning}} with {{Pre-Trained Models}}},
  author = {Zhou, Da-Wei and Cai, Zi-Wen and Ye, Han-Jia and Zhan, De-Chuan and Liu, Ziwei},
  year = {2025},
  month = mar,
  journal = {International Journal of Computer Vision},
  volume = {133},
  number = {3},
  pages = {1012--1032},
  issn = {1573-1405},
  doi = {10.1007/s11263-024-02218-0},
  urldate = {2025-03-09},
  abstract = {Class-incremental learning (CIL) aims to adapt to emerging new classes without forgetting old ones. Traditional CIL models are trained from scratch to continually acquire knowledge as data evolves. Recently, pre-training has achieved substantial progress, making vast pre-trained models (PTMs) accessible for CIL. Contrary to traditional methods, PTMs possess generalizable embeddings, which can be easily transferred for CIL. In this work, we revisit CIL with PTMs and argue that the core factors in CIL are adaptivity for model updating and generalizability for knowledge transferring. (1) We first reveal that frozen PTM can already provide generalizable embeddings for CIL.  Surprisingly, a simple baseline (SimpleCIL) which continually sets the classifiers of PTM to prototype features can beat state-of-the-art even without training on the downstream task. (2) Due to the distribution gap between pre-trained and downstream datasets, PTM can be further cultivated with adaptivity via model adaptation. We propose AdaPt and mERge (Aper), which aggregates the embeddings of PTM and adapted models for classifier construction. Aper is a general framework that can be orthogonally combined with any parameter-efficient tuning method, which holds the advantages of PTM's generalizability and adapted model's adaptivity. (3) Additionally, considering previous ImageNet-based benchmarks are unsuitable in the era of PTM due to data overlapping, we propose four new benchmarks for assessment, namely ImageNet-A, ObjectNet, OmniBenchmark, and VTAB. Extensive experiments validate the effectiveness of Aper with a unified and concise framework. Code is available at https://github.com/zhoudw-zdw/RevisitingCIL.},
  langid = {english},
  keywords = {Artificial Intelligence,Catastrophic forgetting,Class-incremental learning,Continual learning,Pre-trained models},
  file = {C:\Users\steph\Zotero\storage\5RLK63R8\Zhou et al. - 2025 - Revisiting Class-Incremental Learning with Pre-Trained Models Generalizability and Adaptivity are A.pdf}
}

@article{jing2021selfsupervised,
  title = {Self-{{Supervised Visual Feature Learning With Deep Neural Networks}}: {{A Survey}}},
  author = {Jing, Longlong and Tian, Yingli},
  year = {2021},
  journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
  volume = {43},
  number = {11},
  pages = {4037--4058},
  abstract = {Large-scale labeled data are generally required to train deep neural networks in order to obtain better performance in visual feature learning from images or videos for computer vision applications. To avoid extensive cost of collecting and annotating large-scale datasets, as a subset of unsupervised learning methods, self-supervised learning methods are proposed to learn general image and video features from large-scale unlabeled data without using any human-annotated labels. This paper provides an extensive review of deep learning-based self-supervised general visual feature learning methods from images or videos. First, the motivation, general pipeline, and terminologies of this field are described. Then the common deep neural network architectures that used for self-supervised learning are summarized. Next, the schema and evaluation metrics of self-supervised learning methods are reviewed followed by the commonly used datasets for images, videos, audios, and 3D data, as well as the existing self-supervised visual feature learning methods. Finally, quantitative performance comparisons of the reviewed methods on benchmark datasets are summarized and discussed for both image and video feature learning. At last, this paper is concluded and lists a set of promising future directions for self-supervised visual feature learning.},
  eventtitle = {{{IEEE Transactions}} on {{Pattern Analysis}} and {{Machine Intelligence}}},
  keywords = {Annotations,convolutional neural network,deep learning,Feature extraction,Learning systems,Self-supervised learning,Task analysis,Training,transfer learning,unsupervised learning,Videos,Visualization},
  file = {D\:\\herbin\\Zotero\\storage\\X6KD93KT\\Jing et Tian - 2021 - Self-Supervised Visual Feature Learning With Deep .pdf;D\:\\herbin\\Zotero\\storage\\Q4FY5TYI\\9086055.html}
}

@inproceedings{strudel2021segmenter,
  title = {Segmenter: {{Transformer}} for {{Semantic Segmentation}}},
  shorttitle = {Segmenter},
  author = {Strudel, Robin and Garcia, Ricardo and Laptev, Ivan and Schmid, Cordelia},
  year = {2021},
  pages = {7262--7272},
  booktitle = {Proceedings of the {{IEEE}}/{{CVF International Conference}} on {{Computer Vision}}},
  langid = {english},
  file = {D:\herbin\Zotero\storage\8ZQ2XD3L\Strudel et al. - 2021 - Segmenter Transformer for Semantic Segmentation.pdf}
}
@article{wang2025hide,
  title={HIDE-PET: continual learning via hierarchical decomposition of parameter-efficient tuning},
  author={Wang, Liyuan and Xie, Jingyi and Zhang, Xingxing and Su, Hang and Zhu, Jun},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year={2025},
  publisher={IEEE}
}
