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We introduce an evaluation methodology for visual question answering (VQA) to better diagnose cases of shortcut learning.
Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 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
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Neural module networks
Jacob Andreas, Marcus Rohrbach, Trevor Darrell, and Dan Klein · 2016
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”why should i trust you?” explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
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Stacked attention networks for image question answering
Zichao Yang, Xiaodong He, Jianfeng Gao, Li Deng, and Alex Smola · 2016
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Mutan: Multimodal tucker fusion for visual question answering
Hedi Ben-Younes, Remi Cadene, Nicolas Thome, and Matthieu Cord · 2017
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Human attention in visual question answering: Do humans and deep networks look at the same regions?
Abhishek Das, Harsh Agrawal, Larry Zitnick, Devi Parikh, and Dhruv Batra · 2017
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Interpretable explanations of black boxes by meaningful perturbation
Ruth C Fong and Andrea Vedaldi · 2017
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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
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Learning to reason: End-to-end module networks for visual question answering
Ronghang Hu, Jacob Andreas, Marcus Rohrbach, Trevor Darrell, and Kate Saenko · 2017
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Adversarial examples for evaluating reading comprehension systems
Robin Jia and Percy Liang · 2017
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Clevr: A diagnostic dataset for compositional language and elementary visual reasoning
Justin Johnson, Bharath Hariharan, Laurens van der Maaten, Li Fei-Fei, C Lawrence Zitnick, and Ross Girshick · 2017
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An analysis of visual question answering algorithms
Kushal Kafle and Christopher Kanan · 2017
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Show, ask, attend, and answer: A strong baseline for visual question answering
Vahid Kazemi and Ali Elqursh · 2017
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Don’t just assume; look and answer: Overcoming priors for visual question answering
Aishwarya Agrawal, Dhruv Batra, Devi Parikh, and Aniruddha Kembhavi · 2018
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Bottom-up and top-down attention for image captioning and visual question answering
Peter Anderson, Xiaodong He, Chris Buehler, Damien Teney, Mark Johnson, Stephen Gould, and Lei Zhang · 2018
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Gminer: A fast gpu-based frequent itemset mining method for large-scale data
Kang-Wook Chon, Sang-Hyun Hwang, and Min-Soo Kim · 2018
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Overcoming language priors in visual question answering with adversarial regularization
Sainandan Ramakrishnan, Aishwarya Agrawal, and Stefan Lee · 2018
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Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning
Piyush Sharma, Nan Ding, Sebastian Goodman, and Radu Soricut · 2018
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Convnets and imagenet beyond accuracy: Understanding mistakes and uncovering biases
Pierre Stock and Moustapha Cisse · 2018
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Explicit Bias Discovery in Visual Question Answering Models
Varun Manjunatha, Nirat Saini, and Larry S. Davis · 2019
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Right for the wrong reasons: Diagnosing syntactic heuristics in natural language inference
Tom McCoy, Ellie Pavlick, and Tal Linzen · 2019
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Cycle-consistency for robust visual question answering
Meet Shah, Xinlei Chen, Marcus Rohrbach, and Devi Parikh · 2019
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Actively seeking and learning from live data
Damien Teney and Anton van den Hengel · 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
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Underspecification presents challenges for credibility in modern machine learning
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Strike (with) a pose: Neural networks are easily fooled by strange poses of familiar objects
Michael A Alcorn, Qi Li, Zhitao Gong, Chengfei Wang, Long Mai, Wei-Shinn Ku, and Anh Nguyen · 2019
Cited alongside, same era.
Objectnet: A large-scale bias-controlled dataset for pushing the limits of object recognition models
Andrei Barbu, David Mayo, Julian Alverio, William Luo, Christopher Wang, Dan Gutfreund, Josh Tenenbaum, and Boris Katz · 2019
Cited alongside, same era.
Block: Bilinear superdiagonal fusion for visual question answering and visual relationship detection
Hedi Ben-Younes, Remi Cadene, Nicolas Thome, and Matthieu Cord · 2019
Cited alongside, same era.
Approximating cnns with bag-of-local-features models works surprisingly well on imagenet
W. Brendel and M. Bethge · 2019
Cited alongside, same era.
RUBi: Reducing Unimodal Biases for Visual Question Answering
Remi Cadene, Corentin Dancette, Hedi Ben-Younes, Matthieu Cord, and Devi Parikh · 2019
Cited alongside, same era.
Don’t take the easy way out: Ensemble based methods for avoiding known dataset biases
Christopher Clark, Mark Yatskar, and Luke Zettlemoyer · 2019
Cited alongside, same era.
Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness
Robert Geirhos, Patricia Rubisch, Claudio Michaelis, Matthias Bethge, Felix A Wichmann, and Wieland Brendel · 2019
Cited alongside, same era.
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
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Removing bias in multi-modal classifiers: Regularization by maximizing functional entropies
Itai Gat, Idan Schwartz, Alexander Schwing, and Tamir Hazan · 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
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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
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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 · 2020
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Learning from failure: De-biasing classifier from biased classifier
Junhyun Nam, Hyuntak Cha, Sung-Soo Ahn, Jaeho Lee, and Jinwoo Shin · 2020
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A negative case analysis of visual grounding methods for vqa
Robik Shrestha, Kushal Kafle, and Christopher Kanan · 2020
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Unshuffling data for improved generalization
Damien Teney, Ehsan Abbasnejad, and Anton van den Hengel · 2020
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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 · 2020
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