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Despite the great progress of Visual Question Answering (VQA), current VQA models heavily rely on the superficial correlation between the question type and its corresponding frequent answers (i.e., language priors) to make predictions, without really understanding the input.
Analyzing the behavior of visual question answering models
Aishwarya Agrawal, Dhruv Batra, and Devi Parikh. 2016 · 2016
Earlier work this paper cites.
Stacked attention networks for image question answering
Zichao Yang, Xiaodong He, Jianfeng Gao, Li Deng, and Alex Smola. 2016 · 2016
Earlier work this paper cites.
Yin and yang: Balancing and answering binary visual questions
Peng Zhang, Yash Goyal, Douglas Summers-Stay, Dhruv Batra, and Devi Parikh. 2016 · 2016
Earlier work this paper cites.
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
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Focal loss for dense object detection
Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár. 2017 · 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 · 2018
Earlier work this paper cites.
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 · 2018
Earlier work this paper cites.
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.
Don’t take the easy way out: Ensemble based methods for avoiding known dataset biases
Christopher Clark, Mark Yatskar, and Luke Zettlemoyer. 2019 · 2019
Cited alongside, same era.
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
Cited alongside, same era.
Self-critical reasoning for robust visual question answering
Jialin Wu and Raymond Mooney. 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
Later among the works it cites.
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 · 2020
Later among the works it cites.
Loss re-scaling vqa: revisiting the language prior problem from a class-imbalance view
Yangyang Guo, Liqiang Nie, Zhiyong Cheng, Qi Tian, and Min Zhang. 2021 · 2021
Later among the works it cites.
Greedy gradient ensemble for robust visual question answering
Xinzhe Han, Shuhui Wang, Chi Su, Qingming Huang, and Qi Tian. 2021 · 2021
Later among the works it cites.
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
Later among the works it cites.
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Long Chen, Xin Yan, Jun Xiao, Hanwang Zhang, Shiliang Pu, and Yueting Zhuang. 2020 · 2020
Cited alongside, same era.
Overcoming language priors in vqa via decomposed linguistic representations
Chenchen Jing, Yuwei Wu, Xiaoxun Zhang, Yunde Jia, and Qi Wu. 2020 · 2020
Cited alongside, same era.
Yulei Niu and Hanwang Zhang. 2021 · 2021
Later among the works it cites.