Visual Question Answering (original) (raw)

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What is VQA?

VQA is a new dataset containing open-ended questions about images. These questions require an understanding of vision, language and commonsense knowledge to answer.

• 265,016 images (COCO and abstract scenes)
• At least 3 questions (5.4 questions on average) per image
• 10 ground truth answers per question
• 3 plausible (but likely incorrect) answers per question
• Automatic evaluation metric

Dataset

Details on downloading the latest dataset may be found on the download webpage.

• April 2017: Full release (v2.0)

Balanced Real Images

• 204,721 COCO images
  (all of current train/val/test)

• 1,105,904 questions

• 11,059,040 ground truth answers

• ⊕
  March 2017: Beta v1.9 release

Balanced Real Images

• 123,287 COCO images
  (only train/val)
• 658,111 questions
• 6,581,110 ground truth answers
• 1,974,333 plausible answers

Balanced Binary Abstract Scenes

• 31,325 abstract scenes
  (only train/val)

• 33,383 questions

• 333,830 ground truth answers

• ⊕
  October 2015: Full release (v1.0)

Real Images

• 204,721 COCO images
  (all of current train/val/test)
• 614,163 questions
• 6,141,630 ground truth answers
• 1,842,489 plausible answers

Abstract Scenes

• 50,000 abstract scenes

• 150,000 questions

• 1,500,000 ground truth answers

• 450,000 plausible answers

• 250,000 captions

• ⊕
  July 2015: Beta v0.9 release

• 123,287 COCO images (all of train/val)

• 369,861 questions

• 3,698,610 ground truth answers

• 1,109,583 plausible answers

• ⊕
  June 2015: Beta v0.1 release

• 10,000 COCO images (from train)

• 30,000 questions

• 300,000 ground truth answers

• 90,000 plausible answers

Papers

Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering (CVPR 2017)

Download the paper

BibTeX

@InProceedings{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},
booktitle = {Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2017},
}

Yin and Yang: Balancing and Answering Binary Visual Questions (CVPR 2016)

Download the paper

BibTeX

@InProceedings{balanced_binary_vqa,
author = {Peng Zhang and Yash Goyal and Douglas Summers{-}Stay and Dhruv Batra and Devi Parikh},
title = {{Y}in and {Y}ang: Balancing and Answering Binary Visual Questions},
booktitle = {Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2016},
}

VQA: Visual Question Answering (ICCV 2015)

Download the paper

BibTeX

@InProceedings{{VQA},
author = {Stanislaw Antol and Aishwarya Agrawal and Jiasen Lu and Margaret Mitchell and Dhruv Batra and C. Lawrence Zitnick and Devi Parikh},
title = {{VQA}: {V}isual {Q}uestion {A}nswering},
booktitle = {International Conference on Computer Vision (ICCV)},
year = {2015},
}

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