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• W2563399268 abstract "We propose the task of free-form and open-ended Visual Question Answering (VQA). Given an image and a natural language question about the image, the task is to provide an accurate natural language answer. Mirroring real-world scenarios, such as helping the visually impaired, both the questions and answers are open-ended. Visual questions selectively target different areas of an image, including background details and underlying context. As a result, a system that succeeds at VQA typically needs a more detailed understanding of the image and complex reasoning than a system producing generic image captions. Moreover, VQA is amenable to automatic evaluation, since many open-ended answers contain only a few words or a closed set of answers that can be provided in a multiple-choice format. We provide a dataset containing $$sim $$ 0.25 M images, $$sim $$ 0.76 M questions, and $$sim $$ 10 M answers ( www.visualqa.org ), and discuss the information it provides. Numerous baselines and methods for VQA are provided and compared with human performance. Our VQA demo is available on CloudCV ( http://cloudcv.org/vqa )." @default.
• W2563399268 created "2017-01-06" @default.
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• W2563399268 date "2016-11-08" @default.
• W2563399268 modified "2023-10-16" @default.
• W2563399268 title "VQA: Visual Question Answering" @default.
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• W2563399268 doi "https://doi.org/10.1007/s11263-016-0966-6" @default.
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