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Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering.

, , , , , and . International Journal of Computer Vision, 127 (4): 398-414 (2019)

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Generating Diverse Programs with Instruction Conditioned Reinforced Adversarial Learning., , , , , and . CoRR, (2018)C-VQA: A Compositional Split of the Visual Question Answering (VQA) v1.0 Dataset., , , and . CoRR, (2017)Don't Just Assume; Look and Answer: Overcoming Priors for Visual Question Answering., , , and . CoRR, (2017)Visual Storytelling., , , , , , , , , and 5 other author(s). HLT-NAACL, page 1233-1239. The Association for Computational Linguistics, (2016)Overcoming Language Priors in Visual Question Answering with Adversarial Regularization., , and . NeurIPS, page 1548-1558. (2018)VQA: Visual Question Answering., , , , , , and . CoRR, (2015)Measuring Machine Intelligence Through Visual Question Answering., , , , , and . CoRR, (2016)Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering., , , , , and . International Journal of Computer Vision, 127 (4): 398-414 (2019)Analyzing the Behavior of Visual Question Answering Models., , and . EMNLP, page 1955-1960. The Association for Computational Linguistics, (2016)Don't Just Assume; Look and Answer: Overcoming Priors for Visual Question Answering., , , and . CVPR, page 4971-4980. IEEE Computer Society, (2018)