visualqa.org

VQA: Visual Question Answering

Introducing VQA v2.0. A More Balanced and Bigger VQA Dataset! VQA is a new dataset containing open-ended questions about images. These questions require an understanding of vision, language and commonsense knowledge to answer. 254,721 images (MSCOCO and abstract scenes). 3 questions per image (764,163 total). 10 ground truth answers per question. 3 plausible (but likely incorrect) answers per question. Open-ended and multiple-choice answering tasks. 9,934,119 total. To our group for updates!

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VQA: Visual Question Answering | visualqa.org Reviews

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Introducing VQA v2.0. A More Balanced and Bigger VQA Dataset! VQA is a new dataset containing open-ended questions about images. These questions require an understanding of vision, language and commonsense knowledge to answer. 254,721 images (MSCOCO and abstract scenes). 3 questions per image (764,163 total). 10 ground truth answers per question. 3 plausible (but likely incorrect) answers per question. Open-ended and multiple-choice answering tasks. 9,934,119 total. To our group for updates!

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4 demo

5 download

6 evaluation

7 challenge

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9 visualize

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VQA: Visual Question Answering | visualqa.org Reviews

https://visualqa.org

Introducing VQA v2.0. A More Balanced and Bigger VQA Dataset! VQA is a new dataset containing open-ended questions about images. These questions require an understanding of vision, language and commonsense knowledge to answer. 254,721 images (MSCOCO and abstract scenes). 3 questions per image (764,163 total). 10 ground truth answers per question. 3 plausible (but likely incorrect) answers per question. Open-ended and multiple-choice answering tasks. 9,934,119 total. To our group for updates!

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visualqa.org

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VQA v2.0: Visual Question Answering

http://www.visualqa.org/vqa_v2.html

Announcing VQA v2.0. A More Balanced and Bigger VQA Dataset. Article{balanced vqa v2,. Author = {Yash Goyal and Tejas Khot and Douglas Summers-Stay and Dhruv Batra and Devi Parikh},. Title = {Making the {V} in {VQA} Matter: Elevating the Role of Image Understanding in {V}isual {Q}uestion {A}nswering},. Journal = {CoRR},. Volume = {abs/1612.00837},. VQA v2.0 Dataset teaser (More examples). Some examples from a preliminary version of the VQA v2.0 dataset are shown below. You can play with the live interfac...

2

VQA: Visual Question Answering

http://www.visualqa.org/index.html

Introducing VQA v2.0. A More Balanced and Bigger VQA Dataset! VQA is a new dataset containing open-ended questions about images. These questions require an understanding of vision, language and commonsense knowledge to answer. 254,721 images (MSCOCO and abstract scenes). 3 questions per image (764,163 total). 10 ground truth answers per question. 3 plausible (but likely incorrect) answers per question. Open-ended and multiple-choice answering tasks. 9,934,119 total. To our group for updates!

3

VQA: Visual Question Answering

http://www.visualqa.org/external.html

Datasets built on top of VQA. 1 Million Full-Sentences Visual Question Answering (FSVQA). Full-Sentence Visual Question Answering (FSVQA) consists of nearly 1 million pairs of questions and full-sentence answers for images, built by applying a number of rule-based natural language processing techniques to the original VQA dataset and captions in the MS COCO dataset.

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VQA: Visual Question Answering

http://www.visualqa.org/download.html

Training annotations 2015 v1.0. 2,483,490 answers. Validation annotations 2015 v1.0. 1,215,120 answers. Training annotations 2015 v1.0. Validation annotations 2015 v1.0. Training questions 2015 v1.0. Validation questions 2015 v1.0. Testing questions 2015 v1.0. Training questions 2015 v1.0. Validation questions 2015 v1.0. Testing questions 2015 v1.0. Tools (Real and Abstract). The captions for training and validation sets of the abstract scenes can be downloaded from here. LoadQA - Load questions and answ...

5

VQA: Visual Question Answering

http://www.visualqa.org/workshop.html

At CVPR 2017, July 26, Honolulu, Hawaii, USA. The primary purpose of this workshop is to hold the 2nd edition of the Visual Question Answering Challenge on the 2nd edition (v2.0) of the VQA dataset. Introduced in Goyal et al., CVPR 2017. The 1st edition of the VQA Challenge. Was organized in CVPR 2016 on the 1st edition (v1.0) of the VQA dataset. Introduced in Antol et al., ICCV 2015. University of Illinois at Urbana-Champaign. University of Illinois at Urbana-Champaign. University of California, Berkeley.

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filebox.ece.vt.edu

Devi Parikh - Home

https://filebox.ece.vt.edu/~parikh

Allen Distinguished Investigator in Artificial Intelligence. Bradley Department of Electrical and Computer Engineering. Lead: Computer Vision Lab. Core Faculty: Virginia Center for Autonomous Systems. Member: Discovery Analytics Center. Note to prospective students and interns. Office: 440 Whittemore Hall. Lab: 415 Whittemore Hall. 2016: I received a Rowan University Medal of Excellence for Alumni Achievement at Rowan's College of Engineering commencement ceremony. 2016: A little piece. 2015: I received ...

filebox.ece.vt.edu

Dhruv Batra -- Home

https://filebox.ece.vt.edu/~dbatra

Bradley Department of Electrical and Computer Engineering. Machine Learning and Perception Group. Virginia Center for Autonomous Systems. Email: username -at- domain.edu. Where username = dbatra, domain = vt). Prospective and VT students: please read my FAQ. I am serving as an Area Chair for CVPR 2017 and ICLR 2017. Interview with WVTF Radio IQ. For our work on Visual Question Answering. 5 papers accepted at EMNLP16. 4 papers accepted at CVPR16. 2 papers accepted at NAACL16. By the Center for Instruction...

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Margaret Mitchell

http://www.m-mitchell.com/publications.html

Margaret Mitchell - margarmitchell {at} gmail.com. Redmond, WA U.S.A. Mostafazadeh, N. and Misra, I. and Devlin, J. and Zitnick, C. L. and Mitchell, M. and He, X. and Vanderwende, L. (2016). Generating Natural Questions About an Image. Proceedings of ACL 2016. Proceedings of NAACL 2016. Misra, I. and Zitnick, C. L. and Mitchell, M. and Girshick, R. (2016). Seeing through the Human Reporting Bias: Visual Classifiers from Noisy Human-Centric Labels. Proceedings of CVPR 2016. Proceedings of AAAI 2016. Galle...

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VQA: Visual Question Answering

Introducing VQA v2.0. A More Balanced and Bigger VQA Dataset! VQA is a new dataset containing open-ended questions about images. These questions require an understanding of vision, language and commonsense knowledge to answer. 254,721 images (MSCOCO and abstract scenes). 3 questions per image (764,163 total). 10 ground truth answers per question. 3 plausible (but likely incorrect) answers per question. Open-ended and multiple-choice answering tasks. 9,934,119 total. To our group for updates!

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