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Benefiting from large-scale pre-training, we have witnessed significant performance boost on the popular Visual Question Answering (VQA) task.
Vqa: Visual question answering
Stanislaw Antol, Aishwarya Agrawal, Jiasen Lu, Margaret Mitchell, Dhruv Batra, C Lawrence Zitnick, and Devi Parikh · 2015
Earlier work this paper cites.
A large annotated corpus for learning natural language inference
Samuel R Bowman, Gabor Angeli, Christopher Potts, and Christopher D Manning · 2015
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Microsoft coco captions: Data collection and evaluation server
Xinlei Chen, Hao Fang, Tsung-Yi Lin, Ramakrishna Vedantam, Saurabh Gupta, Piotr Dollár, and C Lawrence Zitnick · 2015
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Deep visual-semantic alignments for generating image descriptions
Andrej Karpathy and Li Fei-Fei · 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
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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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Visual genome: Connecting language and vision using crowdsourced dense image annotations
Ranjay Krishna, Yuke Zhu, Oliver Groth, Justin Johnson, Kenji Hata, Joshua Kravitz, Stephanie Chen, Yannis Kalantidis, Li-Jia Li, David A Shamma, et al · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin · 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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Annotation artifacts in natural language inference data
Suchin Gururangan, Swabha Swayamdipta, Omer Levy, Roy Schwartz, Samuel R Bowman, and Noah A Smith · 2018
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Pythia v0. 1: the winning entry to the vqa challenge 2018
Yu Jiang, Vivek Natarajan, Xinlei Chen, Marcus Rohrbach, Dhruv Batra, and Devi Parikh · 2018
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Hypothesis only baselines in natural language inference
Adam Poliak, Jason Naradowsky, Aparajita Haldar, Rachel Rudinger, and Benjamin Van Durme · 2018
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Semantically equivalent adversarial rules for debugging NLP models
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 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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A broad-coverage challenge corpus for sentence understanding through inference
Adina Williams, Nikita Nangia, and Samuel R Bowman · 2018
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Mastering the dungeon: Grounded language learning by mechanical turker descent
Zhilin Yang, Saizheng Zhang, Jack Urbanek, Will Feng, Alexander H Miller, Arthur Szlam, Douwe Kiela, and Jason Weston · 2018
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Swag: A large-scale adversarial dataset for grounded commonsense inference
Rowan Zellers, Yonatan Bisk, Roy Schwartz, and Yejin Choi · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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Dynamic fusion with intra-and inter-modality attention flow for visual question answering
Peng Gao, Zhengkai Jiang, Haoxuan You, Pan Lu, Steven CH Hoi, Xiaogang Wang, and Hongsheng Li · 2019
Cited alongside, same era.
Gqa: a new dataset for compositional question answering over real-world images
Drew A Hudson and Christopher D Manning · 2019
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Relation-aware graph attention network for visual question answering
Linjie Li, Zhe Gan, Yu Cheng, and Jingjing Liu · 2019
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Roberta: A robustly optimized bert pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov · 2019
Cited alongside, same era.
Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks
Jiasen Lu, Dhruv Batra, Devi Parikh, and Stefan Lee · 2019
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Is bert really robust? a strong baseline for natural language attack on text classification and entailment
Di Jin, Zhijing Jin, Joey Tianyi Zhou, and Peter Szolovits · 2020
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Albert: A lite bert for self-supervised learning of language representations
Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, and Radu Soricut · 2020
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What is more likely to happen next? video-and-language future event prediction
Jie Lei, Licheng Yu, Tamara L Berg, and Mohit Bansal · 2020
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A closer look at the robustness of vision-and-language pre-trained models
Linjie Li, Zhe Gan, and Jingjing Liu · 2020
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Textattack: A framework for adversarial attacks, data augmentation, and adversarial training in nlp
John Morris, Eli Lifland, Jin Yong Yoo, Jake Grigsby, Di Jin, and Yanjun Qi · 2020
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Cycle-consistency for robust visual question answering
M Shah, X Chen, M Rohrbach, and D Parikh · 2019
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Lxmert: Learning cross-modality encoder representations from transformers
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Deep modular co-attention networks for visual question answering
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From recognition to cognition: Visual commonsense reasoning
Rowan Zellers, Yonatan Bisk, Ali Farhadi, and Yejin Choi · 2019
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Towards causal vqa: Revealing and reducing spurious correlations by invariant and covariant semantic editing
Vedika Agarwal, Rakshith Shetty, and Mario Fritz · 2020
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Beat the ai: Investigating adversarial human annotation for reading comprehension
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Squinting at vqa models: Interrogating vqa models with sub-questions
Ramprasaath R Selvaraju, Purva Tendulkar, Devi Parikh, Eric Horvitz, Marco Ribeiro, Besmira Nushi, and Ece Kamar · 2020
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Vl-bert: Pre-training of generic visual-linguistic representations
Weijie Su, Xizhou Zhu, Yue Cao, Bin Li, Lewei Lu, Furu Wei, and Jifeng Dai · 2020
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On the value of out-of-distribution testing: An example of goodhart’s law
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Adversarial examples improve image recognition
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Xlnet: Generalized autoregressive pretraining for language understanding
Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, and Quoc V Le · 2020
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Word-level textual adversarial attacking as combinatorial optimization
Yuan Zang, Fanchao Qi, Chenghao Yang, Zhiyuan Liu, Meng Zhang, Qun Liu, and Maosong Sun · 2020
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Roses are red, violets are blue… but should vqa expect them to?
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Less is more: Clipbert for video-and-language learningvia sparse sampling
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Revisiting modulated convolutions for visual counting and beyond
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Vinvl: Revisiting visual representations in vision-language models
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Uc2: Universal cross-lingual cross-modal vision-and-language pre-training
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