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Knowledge-based visual question answering (VQA) involves questions that require world knowledge beyond the image to yield the correct answer.
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Vqa: Visual question answering
Stanislaw Antol, Aishwarya Agrawal, Jiasen Lu, Margaret Mitchell, Dhruv Batra, C Lawrence Zitnick, and Devi Parikh · 2015
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Making the V in VQA matter: Elevating the role of image understanding in Visual Question Answering
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Andrej Karpathy and Li Fei-Fei · 2017
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Conceptnet 5.5: An open multilingual graph of general knowledge
Robyn Speer, Joshua Chin, and Catherine Havasi · 2017
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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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Out of the box: Reasoning with graph convolution nets for factual visual question answering
Medhini Narasimhan, Svetlana Lazebnik, and Alexander Schwing · 2018
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Straight to the facts: Learning knowledge base retrieval for factual visual question answering
Medhini Narasimhan and Alexander G Schwing · 2018
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Jiasen Lu, Dhruv Batra, Devi Parikh, and Stefan Lee · 2019
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Ok-vqa: A visual question answering benchmark requiring external knowledge
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Ok-vqa: A visual question answering benchmark requiring external knowledge
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ConceptBert: Concept-aware representation for visual question answering
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Transform-retrieve-generate: Natural language-centric outside-knowledge visual question answering
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Revive: Regional visual representation matters in knowledge-based visual question answering
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Weakly-supervised visual-retriever-reader for knowledge-based question answering
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Krisp: Integrating implicit and symbolic knowledge for open-domain knowledge-based vqa
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