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Visual Question Answering (VQA) models are prone to learn the shortcut solution formed by dataset biases rather than the intended solution.
Adversarial regularization for visual question answering: Strengths, shortcomings, and side effects
Gabriel Grand and Yonatan Belinkov. 2019 · 1906
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Martin Arjovsky, Léon Bottou, Ishaan Gulrajani, and David Lopez-Paz. 2019 · 1907
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Don’t take the premise for granted: Mitigating artifacts in natural language inference
Yonatan Belinkov, Adam Poliak, Stuart M Shieber, Benjamin Van Durme, and Alexander M Rush. 2019 · 1907
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Lxmert: Learning cross-modality encoder representations from transformers
Hao Tan and Mohit Bansal. 2019 · 1908
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Don’t take the easy way out: Ensemble based methods for avoiding known dataset biases
Christopher Clark, Mark Yatskar, and Luke Zettlemoyer. 2019 · 1909
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Simple but effective techniques to reduce biases
Rabeeh Karimi Mahabadi and James Henderson. 2019 · 1909
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Clever Hans:(the horse of Mr. Von Osten.) a contribution to experimental animal and human psychology
Oskar Pfungst. 1911 · 1911
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Unshuffling data for improved generalization
Damien Teney, Ehsan Abbasnejad, and Anton van den Hengel. 2020a · 2002
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On the value of out-of-distribution testing: An example of goodhart’s law
Damien Teney, Kushal Kafle, Robik Shrestha, Ehsan Abbasnejad, Christopher Kanan, and Anton van den Hengel. 2020b · 2005
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Discriminative learning under covariate shift
Steffen Bickel, Michael Brückner, and Tobias Scheffer. 2009 · 2009
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Mutant: A training paradigm for out-of-distribution generalization in visual question answering
Tejas Gokhale, Pratyay Banerjee, Chitta Baral, and Yezhou Yang. 2020 · 2009
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Impossibility theorems for domain adaptation
Shai Ben David, Tyler Lu, Teresa Luu, and Dávid Pál. 2010 · 2010
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Underspecification presents challenges for credibility in modern machine learning
Alexander D’Amour, Katherine Heller, Dan Moldovan, Ben Adlam, Babak Alipanahi, Alex Beutel, Christina Chen, Jonathan Deaton, Jacob Eisenstein, Matthew D Hoffman, et al. 2020 · 2011
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Unbiased look at dataset bias
Antonio Torralba and Alexei A Efros. 2011 · 2011
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On causal and anticausal learning
Bernhard Schölkopf, Dominik Janzing, Jonas Peters, Eleni Sgouritsa, Kun Zhang, and Joris Mooij. 2012 · 2012
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Overcoming language priors with self-supervised learning for visual question answering
Xi Zhu, Zhendong Mao, Chunxiao Liu, Peng Zhang, Bin Wang, and Yongdong Zhang. 2020 · 2012
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Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher D Manning. 2014 · 2014
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Vqa: Visual question answering
Stanislaw Antol, Aishwarya Agrawal, Jiasen Lu, Margaret Mitchell, Dhruv Batra, C Lawrence Zitnick, and Devi Parikh. 2015 · 2015
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Faster r-cnn: Towards real-time object detection with region proposal networks
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. 2015 · 2015
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Taking a hint: Leveraging explanations to make vision and language models more grounded
Ramprasaath R Selvaraju, Stefan Lee, Yilin Shen, Hongxia Jin, Shalini Ghosh, Larry Heck, Dhruv Batra, and Devi Parikh. 2019 · 2019
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Self-critical reasoning for robust visual question answering
Jialin Wu and Raymond Mooney. 2019 · 2019
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Counterfactual samples synthesizing for robust visual question answering
Long Chen, Xin Yan, Jun Xiao, Hanwang Zhang, Shiliang Pu, and Yueting Zhuang. 2020 · 2020
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Shortcut learning in deep neural networks
Robert Geirhos, Jörn-Henrik Jacobsen, Claudio Michaelis, Richard Zemel, Wieland Brendel, Matthias Bethge, and Felix A Wichmann. 2020 · 2020
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Overcoming language priors in vqa via decomposed linguistic representations
Chenchen Jing, Yuwei Wu, Xiaoxun Zhang, Yunde Jia, and Qi Wu. 2020 · 2020
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Analyzing the behavior of visual question answering models
Aishwarya Agrawal, Dhruv Batra, and Devi Parikh. 2016 · 2016
Cited alongside, same era.
Human behavior and the principle of least effort: An introduction to human ecology
George Kingsley Zipf. 2016 · 2016
Cited alongside, same era.
Making the v in vqa matter: Elevating the role of image understanding in visual question answering
Yash Goyal, Tejas Khot, Douglas Summers-Stay, Dhruv Batra, and Devi Parikh. 2017 · 2017
Cited alongside, same era.
Don’t just assume; look and answer: Overcoming priors for visual question answering
Aishwarya Agrawal, Dhruv Batra, Devi Parikh, and Aniruddha Kembhavi. 2018 · 2018
Cited alongside, same era.
Bilinear attention networks
Jin-Hwa Kim, Jaehyun Jun, and Byoung-Tak Zhang. 2018 · 2018
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Overcoming language priors in visual question answering with adversarial regularization
Sainandan Ramakrishnan, Aishwarya Agrawal, and Stefan Lee. 2018 · 2018
Cited alongside, same era.
Rubi: Reducing unimodal biases for visual question answering
Remi Cadene, Corentin Dancette, Matthieu Cord, Devi Parikh, et al. 2019 · 2019
Cited alongside, same era.
Learning to contrast the counterfactual samples for robust visual question answering
Zujie Liang, Weitao Jiang, Haifeng Hu, and Jiaying Zhu. 2020 · 2020
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A negative case analysis of visual grounding methods for VQA
Robik Shrestha, Kushal Kafle, and Christopher Kanan. 2020 · 2020
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Beyond question-based biases: Assessing multimodal shortcut learning in visual question answering
Corentin Dancette, Remi Cadene, Damien Teney, and Matthieu Cord. 2021 · 2021
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Roses are red, violets are blue… but should vqa expect them to?
Corentin Kervadec, Grigory Antipov, Moez Baccouche, and Christian Wolf. 2021 · 2021
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Lpf: A language-prior feedback objective function for de-biased visual question answering
Zujie Liang, Haifeng Hu, and Jiaying Zhu. 2021 · 2021
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Counterfactual vqa: A cause-effect look at language bias
Yulei Niu, Kaihua Tang, Hanwang Zhang, Zhiwu Lu, Xian-Sheng Hua, and Ji-Rong Wen. 2021 · 2021
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Towards causal representation learning
Bernhard Schölkopf, Francesco Locatello, Stefan Bauer, Nan Rosemary Ke, Nal Kalchbrenner, Anirudh Goyal, and Yoshua Bengio. 2021 · 2021
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Check it again: Progressive visual question answering via visual entailment
Qingyi Si, Zheng Lin, Mingyu Zheng, Peng Fu, and Weiping Wang. 2021 · 2021
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