Heterogeneous Domain Generalization for Single-Source Cross-Dataset Person ReID: An Adaptive Adversarial Augmentation Approach
Keywords: Heterogeneous Domain Generalization, Transfer Learning, Person Re-ID, Cross-Dataset
TL;DR: We introduce an Adaptive Adversarial Augmentation approach, utilizing Generative Adversarial Networks and a dynamic augmentation strategy to improve the recognition of novel classes in unseen domains.
Abstract: Despite the significant advances in supervised person re-identification (ReID) methods, these models exhibit performance degradation in unseen domains. Domain generalization (DG) is applied to alleviate this issue, but most existing DG methods assume consistent class spaces between source and target domains. We propose Adaptive Adversarial Augmentation (AAA), a Heterogeneous Domain Generalization (HDG) approach tailored for single-source cross-dataset ReID. AAA jointly trains a feature extractor alongside a Domain Adversarial Network (DAN) and a Class Adversarial Network (CAN) to enhance the feature extractor's robustness to both domain shifts and class space changes. Additionally, we propose a diversity-based perturbation impact factor, dynamically tuning the perturbation influence aligned with the diversity of learned embeddings, thus providing a flexible augmentation strategy. Experimental results demonstrate that our method surpasses state-of-the-art methods on large-scale cross-dataset ReID benchmarks.
Primary Area: transfer learning, meta learning, and lifelong learning
Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics.
Submission Guidelines: I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2024/AuthorGuide.
Anonymous Url: I certify that there is no URL (e.g., github page) that could be used to find authors' identity.
No Acknowledgement Section: I certify that there is no acknowledgement section in this submission for double blind review.
Submission Number: 1771
Loading