Fetching the paper…
Reading the bibliography…
While sophisticated Visual Question Answering models have achieved remarkable success, they tend to answer questions only according to superficial correlations between question and answer.
Visual entailment: A novel task for fine-grained image understanding
Ning Xie, Farley Lai, Derek Doran, and Asim Kadav. 2019 · 1901
Earlier work this paper cites.
Visualbert: A simple and performant baseline for vision and language
Liunian Harold Li, Mark Yatskar, Da Yin, Cho-Jui Hsieh, and Kai-Wei Chang. 2019 · 1908
Earlier work this paper cites.
Simple but effective techniques to reduce biases
Rabeeh Karimi Mahabadi and James Henderson. 2019 · 1909
Earlier work this paper 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 · 2012
Earlier work this paper cites.
Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher D Manning. 2014 · 2014
Earlier work this paper cites.
Vqa: Visual question answering
Stanislaw Antol, Aishwarya Agrawal, Jiasen Lu, Margaret Mitchell, Dhruv Batra, C Lawrence Zitnick, and Devi Parikh. 2015 · 2015
Earlier work this paper cites.
Analyzing the behavior of visual question answering models
Aishwarya Agrawal, Dhruv Batra, and Devi Parikh. 2016 · 2016
Earlier work this paper cites.
Revisiting visual question answering baselines
Allan Jabri, Armand Joulin, and Laurens Van Der Maaten. 2016 · 2016
Earlier work this paper cites.
Densecap: Fully convolutional localization networks for dense captioning
Justin Johnson, Andrej Karpathy, and Li Fei-Fei. 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
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.
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
Cited alongside, same era.
Overcoming language priors in visual question answering with adversarial regularization
Sainandan Ramakrishnan, Aishwarya Agrawal, and Stefan Lee. 2018 · 2018
Cited alongside, same era.
Adversarial regularization for visual question answering: Strengths, shortcomings, and side effects
Gabriel Grand and Yonatan Belinkov. 2019 · 2019
Later among the works it cites.
Rankqa: Neural question answering with answer re-ranking
Bernhard Kratzwald, Anna Eigenmann, and Stefan Feuerriegel. 2019 · 2019
Later among the works it cites.
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
Later among the works it cites.
Lxmert: Learning cross-modality encoder representations from transformers
Hao Tan and Mohit Bansal. 2019 · 2019
Later among the works it cites.
Mutant: A training paradigm for out-of-distribution generalization in visual question answering
Tejas Gokhale, Pratyay Banerjee, Chitta Baral, and Yezhou Yang. 2020 · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
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 · 2019
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.
Counterfactual samples synthesizing for robust visual question answering
Long Chen, Xin Yan, Jun Xiao, Hanwang Zhang, Shiliang Pu, and Yueting Zhuang. 2020a
Cited in the paper.
Uniter: Universal image-text representation learning
Yen-Chun Chen, Linjie Li, Licheng Yu, Ahmed El Kholy, Faisal Ahmed, Zhe Gan, Yu Cheng, and Jingjing Liu. 2020b
Cited in the paper.
Evidence aggregation for answer re-ranking in open-domain question answering
Shuohang Wang, Mo Yu, Jing Jiang, Wei Zhang, Xiaoxiao Guo, Shiyu Chang, Zhiguo Wang, Tim Klinger, Gerald Tesauro, and Murray Campbell. 2018a
Cited in the paper.
Joint training of candidate extraction and answer selection for reading comprehension
Zhen Wang, Jiachen Liu, Xinyan Xiao, Yajuan Lyu, and Tian Wu. 2018b
Cited in the paper.
Oscar: Object-semantics aligned pre-training for vision-language tasks
Xiujun Li, Xi Yin, Chunyuan Li, Pengchuan Zhang, Xiaowei Hu, Lei Zhang, Lijuan Wang, Houdong Hu, Li Dong, Furu Wei, et al. 2020 · 2020
Later among the works it cites.
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.
Rankvqa: Answer re-ranking for visual question answering
Yanyuan Qiao, Zheng Yu, and Jing Liu. 2020 · 2020
Later among the works it cites.