\begin{thebibliography}{41}
\providecommand{\natexlab}[1]{#1}
\providecommand{\url}[1]{\texttt{#1}}
\expandafter\ifx\csname urlstyle\endcsname\relax
  \providecommand{\doi}[1]{doi: #1}\else
  \providecommand{\doi}{doi: \begingroup \urlstyle{rm}\Url}\fi

\bibitem[Abbasnejad et~al.(2020)Abbasnejad, Teney, Parvaneh, Shi, and
  Hengel]{ref44}
Ehsan Abbasnejad, Damien Teney, Amin Parvaneh, Javen Shi, and Anton van~den
  Hengel.
\newblock Counterfactual vision and language learning.
\newblock In \emph{Proceedings of the IEEE/CVF conference on computer vision
  and pattern recognition}, pages 10044--10054, 2020.

\bibitem[Agrawal et~al.(2016)Agrawal, Batra, and Parikh]{ref6}
Aishwarya Agrawal, Dhruv Batra, and Devi Parikh.
\newblock Analyzing the behavior of visual question answering models.
\newblock In \emph{Proceedings of the 2016 Conference on Empirical Methods in
  Natural Language Processing, {EMNLP} 2016, Austin, Texas, USA, November 1-4,
  2016}, pages 1955--1960, 2016.

\bibitem[Agrawal et~al.(2018)Agrawal, Batra, Parikh, and Kembhavi]{ref10}
Aishwarya Agrawal, Dhruv Batra, Devi Parikh, and Aniruddha Kembhavi.
\newblock Don't just assume; look and answer: Overcoming priors for visual
  question answering.
\newblock In \emph{Proceedings of the IEEE conference on computer vision and
  pattern recognition}, pages 4971--4980, 2018.

\bibitem[Anderson et~al.(2018)Anderson, He, Buehler, Teney, Johnson, Gould, and
  Zhang]{ref3}
Peter Anderson, Xiaodong He, Chris Buehler, Damien Teney, Mark Johnson, Stephen
  Gould, and Lei Zhang.
\newblock Bottom-up and top-down attention for image captioning and visual
  question answering.
\newblock In \emph{Proceedings of the IEEE conference on computer vision and
  pattern recognition}, pages 6077--6086, 2018.

\bibitem[Antol et~al.(2015)Antol, Agrawal, Lu, Mitchell, Batra, Zitnick, and
  Parikh]{ref1}
Stanislaw Antol, Aishwarya Agrawal, Jiasen Lu, Margaret Mitchell, Dhruv Batra,
  C~Lawrence Zitnick, and Devi Parikh.
\newblock Vqa: Visual question answering.
\newblock In \emph{Proceedings of the IEEE international conference on computer
  vision}, pages 2425--2433, 2015.

\bibitem[Cadene et~al.(2019)Cadene, Dancette, Cord, Parikh, et~al.]{ref11}
Remi Cadene, Corentin Dancette, Matthieu Cord, Devi Parikh, et~al.
\newblock Rubi: Reducing unimodal biases for visual question answering.
\newblock \emph{Advances in neural information processing systems},
  32:\penalty0 839--850, 2019.

\bibitem[Chen et~al.(2020)Chen, Yan, Xiao, Zhang, Pu, and Zhuang]{ref32}
Long Chen, Xin Yan, Jun Xiao, Hanwang Zhang, Shiliang Pu, and Yueting Zhuang.
\newblock Counterfactual samples synthesizing for robust visual question
  answering.
\newblock In \emph{Proceedings of the IEEE/CVF conference on computer vision
  and pattern recognition}, pages 10800--10809, 2020.

\bibitem[Clark et~al.(2019)Clark, Yatskar, and Zettlemoyer]{ref31}
Christopher Clark, Mark Yatskar, and Luke Zettlemoyer.
\newblock Don't take the easy way out: Ensemble based methods for avoiding
  known dataset biases.
\newblock In \emph{Proceedings of the 2019 Conference on Empirical Methods in
  Natural Language Processing and the 9th International Joint Conference on
  Natural Language Processing}, pages 4067--4080, 2019.

\bibitem[Das et~al.(2017)Das, Agrawal, Zitnick, Parikh, and Batra]{ref41}
Abhishek Das, Harsh Agrawal, Larry Zitnick, Devi Parikh, and Dhruv Batra.
\newblock Human attention in visual question answering: Do humans and deep
  networks look at the same regions?
\newblock \emph{Computer Vision and Image Understanding}, 163:\penalty0
  90--100, 2017.

\bibitem[Gao et~al.(2018)Gao, Zeng, Song, Liu, and Shen]{ref15}
Lianli Gao, Pengpeng Zeng, Jingkuan Song, Xianglong Liu, and Heng~Tao Shen.
\newblock Examine before you answer: Multi-task learning with
  adaptive-attentions for multiple-choice vqa.
\newblock In \emph{Proceedings of the 26th ACM international conference on
  Multimedia}, pages 1742--1750, 2018.

\bibitem[Goyal et~al.(2017)Goyal, Khot, Summers-Stay, Batra, and Parikh]{ref7}
Yash Goyal, Tejas Khot, Douglas Summers-Stay, Dhruv Batra, and Devi Parikh.
\newblock Making the v in vqa matter: Elevating the role of image understanding
  in visual question answering.
\newblock In \emph{Proceedings of the IEEE conference on computer vision and
  pattern recognition}, pages 6904--6913, 2017.

\bibitem[Guo et~al.(2022)Guo, Nie, Cheng, Tian, and Zhang]{ref39}
Yangyang Guo, Liqiang Nie, Zhiyong Cheng, Qi~Tian, and Min Zhang.
\newblock Loss re-scaling vqa: Revisiting the language prior problem from a
  class-imbalance view.
\newblock \emph{IEEE transactions on image processing: a publication of the
  IEEE Signal Processing Society}, 31:\penalty0 227--238, 2022.

\bibitem[Han et~al.(2021)Han, Wang, Su, Huang, and Tian]{ref38}
Xinzhe Han, Shuhui Wang, Chi Su, Qingming Huang, and Qi~Tian.
\newblock Greedy gradient ensemble for robust visual question answering.
\newblock In \emph{Proceedings of the IEEE/CVF International Conference on
  Computer Vision}, pages 1584--1593, 2021.

\bibitem[Huang et~al.(2020)Huang, Wei, Cai, Zheng, Chen, Leung, and Li]{ref5}
Qingbao Huang, Jielong Wei, Yi~Cai, Changmeng Zheng, Junying Chen, Ho-fung
  Leung, and Qing Li.
\newblock Aligned dual channel graph convolutional network for visual question
  answering.
\newblock In \emph{Proceedings of the 58th annual meeting of the association
  for computational linguistics}, pages 7166--7176, 2020.

\bibitem[Hudson and Manning(2019)]{ref9}
Drew~A Hudson and Christopher~D Manning.
\newblock Gqa: A new dataset for real-world visual reasoning and compositional
  question answering.
\newblock In \emph{Proceedings of the IEEE/CVF conference on computer vision
  and pattern recognition}, pages 6700--6709, 2019.

\bibitem[Khademi(2020)]{ref17}
Mahmoud Khademi.
\newblock Multimodal neural graph memory networks for visual question
  answering.
\newblock In \emph{Proceedings of the 58th Annual Meeting of the Association
  for Computational Linguistics}, pages 7177--7188, 2020.

\bibitem[Kim et~al.(2018)Kim, Jun, and Zhang]{ref16}
Jin-Hwa Kim, Jaehyun Jun, and Byoung-Tak Zhang.
\newblock Bilinear attention networks.
\newblock \emph{Advances in neural information processing systems},
  31:\penalty0 1571--1581, 2018.

\bibitem[Lao et~al.(2021)Lao, Guo, Liu, and Lew]{ref24}
Mingrui Lao, Yanming Guo, Yu~Liu, and Michael~S Lew.
\newblock A language prior based focal loss for visual question answering.
\newblock In \emph{2021 IEEE International Conference on Multimedia and Expo
  (ICME)}, pages 1--6, 2021.

\bibitem[Li et~al.(2019)Li, Gan, Cheng, and Liu]{ref14}
Linjie Li, Zhe Gan, Yu~Cheng, and Jingjing Liu.
\newblock Relation-aware graph attention network for visual question answering.
\newblock In \emph{Proceedings of the IEEE/CVF international conference on
  computer vision}, pages 10313--10322, 2019.

\bibitem[Liang et~al.(2020)Liang, Jiang, Hu, and Zhu]{ref36}
Zujie Liang, Weitao Jiang, Haifeng Hu, and Jiaying Zhu.
\newblock Learning to contrast the counterfactual samples for robust visual
  question answering.
\newblock In \emph{Proceedings of the 2020 conference on empirical methods in
  natural language processing (EMNLP)}, pages 3285--3292, 2020.

\bibitem[Liang et~al.(2021)Liang, Hu, and Zhu]{ref23}
Zujie Liang, Haifeng Hu, and Jiaying Zhu.
\newblock Lpf: a language-prior feedback objective function for de-biased
  visual question answering.
\newblock In \emph{Proceedings of the 44th International ACM SIGIR Conference
  on Research and Development in Information Retrieval}, pages 1955--1959,
  2021.

\bibitem[Mahabadi et~al.(2020)Mahabadi, Belinkov, and Henderson]{ref45}
Rabeeh~Karimi Mahabadi, Yonatan Belinkov, and James Henderson.
\newblock End-to-end bias mitigation by modelling biases in corpora.
\newblock In \emph{Proceedings of the 58th Annual Meeting of the Association
  for Computational Linguistics}, pages 8706--8716, 2020.

\bibitem[Niu et~al.(2021)Niu, Tang, Zhang, Lu, Hua, and Wen]{ref22}
Yulei Niu, Kaihua Tang, Hanwang Zhang, Zhiwu Lu, Xian-Sheng Hua, and Ji-Rong
  Wen.
\newblock Counterfactual vqa: A cause-effect look at language bias.
\newblock In \emph{Proceedings of the IEEE/CVF Conference on Computer Vision
  and Pattern Recognition}, pages 12700--12710, 2021.

\bibitem[Ouyang et~al.(2022)Ouyang, Huang, Li, Cai, Liu, Leung, and Li]{ref25}
Ninglin Ouyang, Qingbao Huang, Pijian Li, Yi~Cai, Bin Liu, Ho-fung Leung, and
  Qing Li.
\newblock Suppressing biased samples for robust vqa.
\newblock \emph{IEEE Transactions on Multimedia}, 24:\penalty0 3405--3415,
  2022.

\bibitem[Ramakrishnan et~al.(2018)Ramakrishnan, Agrawal, and Lee]{ref37}
Sainandan Ramakrishnan, Aishwarya Agrawal, and Stefan Lee.
\newblock Overcoming language priors in visual question answering with
  adversarial regularization.
\newblock \emph{Advances in Neural Information Processing Systems},
  31:\penalty0 1548--1558, 2018.

\bibitem[Selvaraju et~al.(2019)Selvaraju, Lee, Shen, Jin, Ghosh, Heck, Batra,
  and Parikh]{ref12}
Ramprasaath~R Selvaraju, Stefan Lee, Yilin Shen, Hongxia Jin, Shalini Ghosh,
  Larry Heck, Dhruv Batra, and Devi Parikh.
\newblock Taking a hint: Leveraging explanations to make vision and language
  models more grounded.
\newblock In \emph{Proceedings of the IEEE/CVF international conference on
  computer vision}, pages 2591--2600, 2019.

\bibitem[Shrestha et~al.(2020)Shrestha, Kafle, and Kanan]{ref42}
Robik Shrestha, Kushal Kafle, and Christopher Kanan.
\newblock A negative case analysis of visual grounding methods for {VQA}.
\newblock In \emph{Proceedings of the 58th Annual Meeting of the Association
  for Computational Linguistics}, pages 8172--8181, 2020.

\bibitem[Teney et~al.(2020{\natexlab{a}})Teney, Abbasnedjad, and van~den
  Hengel]{ref43}
Damien Teney, Ehsan Abbasnedjad, and Anton van~den Hengel.
\newblock Learning what makes a difference from counterfactual examples and
  gradient supervision.
\newblock In \emph{Proceedings of Computer Vision--ECCV 2020}, pages 580--599,
  2020{\natexlab{a}}.

\bibitem[Teney et~al.(2020{\natexlab{b}})Teney, Abbasnejad, Kafle, Shrestha,
  Kanan, and Van Den~Hengel]{ref35}
Damien Teney, Ehsan Abbasnejad, Kushal Kafle, Robik Shrestha, Christopher
  Kanan, and Anton Van Den~Hengel.
\newblock On the value of out-of-distribution testing: An example of goodhart's
  law.
\newblock \emph{Advances in Neural Information Processing Systems},
  33:\penalty0 407--417, 2020{\natexlab{b}}.

\bibitem[Teney et~al.(2021)Teney, Abbasnejad, and van~den Hengel]{ref34}
Damien Teney, Ehsan Abbasnejad, and Anton van~den Hengel.
\newblock Unshuffling data for improved generalization in visual question
  answering.
\newblock In \emph{Proceedings of the IEEE/CVF international conference on
  computer vision}, pages 1417--1427, 2021.

\bibitem[Vaswani et~al.(2017)Vaswani, Shazeer, Parmar, Uszkoreit, Jones, Gomez,
  Kaiser, and Polosukhin]{ref26}
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones,
  Aidan~N Gomez, {\L}ukasz Kaiser, and Illia Polosukhin.
\newblock Attention is all you need.
\newblock \emph{Advances in neural information processing systems},
  30:\penalty0 5998--6008, 2017.

\bibitem[Wen et~al.(2021)Wen, Xu, Tan, Wu, and Wu]{ref29}
Zhiquan Wen, Guanghui Xu, Mingkui Tan, Qingyao Wu, and Qi~Wu.
\newblock Debiased visual question answering from feature and sample
  perspectives.
\newblock \emph{Advances in Neural Information Processing Systems},
  34:\penalty0 3784--3796, 2021.

\bibitem[Wu and Mooney(2019)]{ref33}
Jialin Wu and Raymond Mooney.
\newblock Self-critical reasoning for robust visual question answering.
\newblock \emph{Advances in Neural Information Processing Systems},
  32:\penalty0 8601--8611, 2019.

\bibitem[Wu et~al.(2016)Wu, Wang, Shen, Dick, and Van Den~Hengel]{ref18}
Qi~Wu, Peng Wang, Chunhua Shen, Anthony Dick, and Anton Van Den~Hengel.
\newblock Ask me anything: Free-form visual question answering based on
  knowledge from external sources.
\newblock In \emph{Proceedings of the IEEE conference on computer vision and
  pattern recognition}, pages 4622--4630, 2016.

\bibitem[Yang et~al.(2021)Yang, Feng, Li, Shen, Wang, and Jiang]{ref40}
Chao Yang, Su~Feng, Dongsheng Li, Huawei Shen, Guoqing Wang, and Bin Jiang.
\newblock Learning content and context with language bias for visual question
  answering.
\newblock In \emph{2021 IEEE International Conference on Multimedia and Expo
  (ICME)}, pages 1--6, 2021.

\bibitem[Yang et~al.(2016)Yang, He, Gao, Deng, and Smola]{ref21}
Zichao Yang, Xiaodong He, Jianfeng Gao, Li~Deng, and Alex Smola.
\newblock Stacked attention networks for image question answering.
\newblock In \emph{Proceedings of the IEEE conference on computer vision and
  pattern recognition}, pages 21--29, 2016.

\bibitem[Yu et~al.(2019)Yu, Yu, Cui, Tao, and Tian]{ref4}
Zhou Yu, Jun Yu, Yuhao Cui, Dacheng Tao, and Qi~Tian.
\newblock Deep modular co-attention networks for visual question answering.
\newblock In \emph{Proceedings of the IEEE/CVF conference on computer vision
  and pattern recognition}, pages 6281--6290, 2019.

\bibitem[Zhang et~al.(2020)Zhang, Liu, Liu, Zeng, Li, Song, and Gao]{ref19}
Liyang Zhang, Shuaicheng Liu, Donghao Liu, Pengpeng Zeng, Xiangpeng Li,
  Jingkuan Song, and Lianli Gao.
\newblock Rich visual knowledge-based augmentation network for visual question
  answering.
\newblock \emph{IEEE Transactions on Neural Networks and Learning Systems},
  32\penalty0 (10):\penalty0 4362--4373, 2020.

\bibitem[Zhang et~al.(2016)Zhang, Goyal, Summers-Stay, Batra, and Parikh]{ref8}
Peng Zhang, Yash Goyal, Douglas Summers-Stay, Dhruv Batra, and Devi Parikh.
\newblock Yin and yang: Balancing and answering binary visual questions.
\newblock In \emph{Proceedings of the IEEE conference on computer vision and
  pattern recognition}, pages 5014--5022, 2016.

\bibitem[Zhang et~al.(2019)Zhang, Tang, Cao, Pu, Wu, and Zhuang]{ref2}
Wenqiao Zhang, Siliang Tang, Yanpeng Cao, Shiliang Pu, Fei Wu, and Yueting
  Zhuang.
\newblock Frame augmented alternating attention network for video question
  answering.
\newblock \emph{IEEE Transactions on Multimedia}, 22\penalty0 (4):\penalty0
  1032--1041, 2019.

\bibitem[Zhu et~al.(2020)Zhu, Mao, Liu, Zhang, Wang, and Zhang]{ref13}
Xi~Zhu, Zhendong Mao, Chunxiao Liu, Peng Zhang, Bin Wang, and Yongdong Zhang.
\newblock Overcoming language priors with self-supervised learning for visual
  question answering.
\newblock In \emph{Proceedings of the Twenty-Ninth International Joint
  Conference on Artificial Intelligence}, pages 1083--1089, 2020.

\end{thebibliography}
