Multiple-choice Visual Question Answering

Multiple-Choice Visual Question Answering is a Visual Question Answering task in which a model selects the correct answer to a question about an image from a predefined set of candidate options.

26

Publications

9.3K

Citations

100

Authors

54

Institutions

Publications per year

2017–2026

13

Variants

Mc-vqa

Authors

100

  1. AA
    Aishwarya Agrawal

    Virginia Tech, United States

    3 publications · 6K citations

    3

    h-index

  2. MM
    Margaret Mitchell

    Google (United States), United States

    3 publications · 6K citations

    3

    h-index

  3. CL
    C. Lawrence Zitnick

    Microsoft (United States), United States

    3 publications · 6K citations

    3

    h-index

  4. DB
    Dhruv Batra

    Georgia Institute of Technology, United States

    3 publications · 6K citations

    3

    h-index

  5. JL
    Jiasen Lu

    Georgia Institute of Technology, United States

    3 publications · 6K citations

    3

    h-index

1–5 of 100

Venues

  1. arXiv (Cornell University)

    Repository · 6 publications · 2.5K citations

    6

    h-index

  2. IEEE Transactions on Image Processing

    2 publications · 50 citations

    2

    h-index

  3. Medical Education

    1 publications · 275 citations

    1

    h-index

  4. npj Digital Medicine

    1 publications · 113 citations

    1

    h-index

  5. Journal of Visual Impairment & Blindness

    JVIB · 1 publications · 20 citations

    1

    h-index

1–5