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VQA: Visual Question Answering: www.visualqa.org

International journal of computer vision, 2017-05, Vol.123 (1), p.4-31 [Peer Reviewed Journal]

Springer Science+Business Media New York 2016 ;International Journal of Computer Vision is a copyright of Springer, 2017. ;ISSN: 0920-5691 ;EISSN: 1573-1405 ;DOI: 10.1007/s11263-016-0966-6

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Title:
VQA: Visual Question Answering: www.visualqa.org
Author: Agrawal, Aishwarya ; Lu, Jiasen ; Antol, Stanislaw ; Mitchell, Margaret ; Zitnick, C. Lawrence ; Parikh, Devi ; Batra, Dhruv
Subjects: Algorithms ; Artificial Intelligence ; Computer Imaging ; Computer Science ; Computer vision ; Datasets ; Human performance ; Image Processing and Computer Vision ; Image processing systems ; Information dissemination ; Language ; Natural language ; Natural language (computers) ; Natural language processing ; Pattern Recognition ; Pattern Recognition and Graphics ; Questioning ; Studies ; Tasks ; Texts ; Vision ; Vision systems
Is Part Of: International journal of computer vision, 2017-05, Vol.123 (1), p.4-31
Description: 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 ∼ 0.25 M images, ∼ 0.76 M questions, and ∼ 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 ).
Publisher: New York: Springer US
Language: English
Identifier: ISSN: 0920-5691
EISSN: 1573-1405
DOI: 10.1007/s11263-016-0966-6
Source: ProQuest Central

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VQA: Visual Question Answering: www.visualqa.org

Springer Science+Business Media New York 2016 ;International Journal of Computer Vision is a copyright of Springer, 2017. ;ISSN: 0920-5691 ;EISSN: 1573-1405 ;DOI: 10.1007/s11263-016-0966-6

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