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Visual question answering (VQA) is a hallmark of vision and language reasoning and a challenging task under the zero-shot setting.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020 · 1901
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Roberta: A robustly optimized bert pretraining approach
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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
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Unifying vision-and-language tasks via text generation
Jaemin Cho, Jie Lei, Hao Tan, and Mohit Bansal. 2021 · 1942
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Microsoft COCO: common objects in context
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Controlling linguistic style aspects in neural language generation
Jessica Ficler and Yoav Goldberg. 2017 · 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 · 2017
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Grad-CAM: Visual explanations from deep networks via gradient-based localization
Ramprasaath R. Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra. 2017 · 2017
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Learning with latent language
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Drew A. Hudson and Christopher D. Manning. 2019 · 2019
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Finetuned language models are zero-shot learners
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Natural language deduction through search over statement compositions
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