Visual Question Answering

Visual Question Answering is a multidisciplinary task in computer vision and natural language processing that requires a system to generate accurate and contextually appropriate answers to natural language questions based on the content of a given image.

2.9K

Publications

257.4K

Citations

8.8K

Authors

1.4K

Institutions

Publications per year

2017–2026

2.4K

Authors

8.8K

  1. DP
    Devi Parikh

    Georgia Institute of Technology, United States

    55 publications · 37.3K citations

    33

    h-index

  2. QW
    Qi Wu

    The University of Adelaide, Australia

    45 publications · 4.6K citations

    33

    h-index

  3. DB
    Dhruv Batra

    Georgia Institute of Technology, United States

    52 publications · 37.2K citations

    32

    h-index

  4. AV
    Anton van den Hengel

    The University of Adelaide, Australia

    41 publications · 4.1K citations

    29

    h-index

  5. ZG
    Zhe Gan

    Microsoft Research (United Kingdom), United Kingdom

    30 publications · 3.7K citations

    27

    h-index

1–5 of 8.8K

Venues

  1. arXiv (Cornell University)

    Repository · 497 publications · 47.4K citations

    92

    h-index

  2. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 80 publications · 8.3K citations

    40

    h-index

  3. Pattern Recognition

    55 publications · 2.8K citations

    31

    h-index

  4. Neurocomputing

    73 publications · 2.9K citations

    31

    h-index

  5. IEEE Transactions on Image Processing

    31 publications · 2.2K citations

    26

    h-index

1–5