VQA: Visual Question Answering: www.visualqa.org
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 a...
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| Veröffentlicht in: | International journal of computer vision 2017-05, Vol.123 (1), p.4-31 |
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| container_title | International journal of computer vision |
| container_volume | 123 |
| creator | Agrawal, Aishwarya Lu, Jiasen Antol, Stanislaw Mitchell, Margaret Zitnick, C. Lawrence Parikh, Devi Batra, Dhruv |
| 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
). |
| doi_str_mv | 10.1007/s11263-016-0966-6 |
| format | Article |
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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
).</description><identifier>ISSN: 0920-5691</identifier><identifier>EISSN: 1573-1405</identifier><identifier>DOI: 10.1007/s11263-016-0966-6</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>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 ; Pizza ; Questioning ; Studies ; Tasks ; Texts ; Vision ; Vision systems</subject><ispartof>International journal of computer vision, 2017-05, Vol.123 (1), p.4-31</ispartof><rights>Springer Science+Business Media New York 2016</rights><rights>International Journal of Computer Vision is a copyright of Springer, 2017.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c2166-855605d0efba32499a2222e2efca4c0491b5b0022b0547b374215a414a218d593</cites><orcidid>0000-0002-8620-8077</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11263-016-0966-6$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11263-016-0966-6$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27901,27902,41464,42533,51294</link.rule.ids></links><search><creatorcontrib>Agrawal, Aishwarya</creatorcontrib><creatorcontrib>Lu, Jiasen</creatorcontrib><creatorcontrib>Antol, Stanislaw</creatorcontrib><creatorcontrib>Mitchell, Margaret</creatorcontrib><creatorcontrib>Zitnick, C. Lawrence</creatorcontrib><creatorcontrib>Parikh, Devi</creatorcontrib><creatorcontrib>Batra, Dhruv</creatorcontrib><title>VQA: Visual Question Answering: www.visualqa.org</title><title>International journal of computer vision</title><addtitle>Int J Comput Vis</addtitle><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
).</description><subject>Algorithms</subject><subject>Artificial Intelligence</subject><subject>Computer Imaging</subject><subject>Computer Science</subject><subject>Computer vision</subject><subject>Datasets</subject><subject>Human performance</subject><subject>Image Processing and Computer Vision</subject><subject>Image processing systems</subject><subject>Information dissemination</subject><subject>Language</subject><subject>Natural language</subject><subject>Natural language (computers)</subject><subject>Natural language processing</subject><subject>Pattern Recognition</subject><subject>Pattern Recognition and Graphics</subject><subject>Pizza</subject><subject>Questioning</subject><subject>Studies</subject><subject>Tasks</subject><subject>Texts</subject><subject>Vision</subject><subject>Vision systems</subject><issn>0920-5691</issn><issn>1573-1405</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNp1kM1Lw0AQxRdRsFb_AC9S8OJldWb2I1lvpfgFBSlor8sm3UhKmtRdg_jfuyEeRHAuc_m992YeY-cI1wiQ3URE0oIDag5Ga64P2ARVJjhKUIdsAoaAK23wmJ3EuAUAyklM2MV6Nb-drevYu2a26n38qLt2Nm_jpw91-3bKjirXRH_2s6fs9f7uZfHIl88PT4v5kpeEKS1XSoPagK8KJ0ga4yiNJ1-VTpYgDRaqSJFUgJJZITJJqJxE6QjzjTJiyq5G333o3ocr7K6OpW8a1_qujxYNSJIZGp3Qyz_otutDm66zmBtCQQlKFI5UGboYg6_sPtQ7F74sgh0as2NjNjVmh8bs4EyjJu6H33345fyv6BsSFGmI</recordid><startdate>20170501</startdate><enddate>20170501</enddate><creator>Agrawal, Aishwarya</creator><creator>Lu, Jiasen</creator><creator>Antol, Stanislaw</creator><creator>Mitchell, Margaret</creator><creator>Zitnick, C. 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Lawrence</au><au>Parikh, Devi</au><au>Batra, Dhruv</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>VQA: Visual Question Answering: www.visualqa.org</atitle><jtitle>International journal of computer vision</jtitle><stitle>Int J Comput Vis</stitle><date>2017-05-01</date><risdate>2017</risdate><volume>123</volume><issue>1</issue><spage>4</spage><epage>31</epage><pages>4-31</pages><issn>0920-5691</issn><eissn>1573-1405</eissn><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
∼
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
).</abstract><cop>New York</cop><pub>Springer US</pub><doi>10.1007/s11263-016-0966-6</doi><tpages>28</tpages><orcidid>https://orcid.org/0000-0002-8620-8077</orcidid></addata></record> |
| fulltext | fulltext |
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| ispartof | International journal of computer vision, 2017-05, Vol.123 (1), p.4-31 |
| issn | 0920-5691 1573-1405 |
| language | eng |
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| source | SpringerLink Journals |
| 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 Pizza Questioning Studies Tasks Texts Vision Vision systems |
| title | VQA: Visual Question Answering: www.visualqa.org |
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