Explainable Visual Question Answering

Explainable Visual Question Answering is a visual question answering paradigm that, as an extension of standard visual question answering, incorporates mechanisms to generate human-interpretable justifications or rationales for its answers, thereby exposing the reasoning process and grounding of multimodal inferences.

57

Publications

3.5K

Citations

172

Authors

66

Institutions

Publications per year

2017–2026

54

Variants

Explainable Vqa

Authors

172

  1. JX
    Jun Xiao

    Zhejiang University, China

    4 publications · 421 citations

    4

    h-index

  2. DP
    Devi Parikh

    Georgia Institute of Technology, United States

    4 publications · 440 citations

    4

    h-index

  3. JL
    Jiebo Luo

    University of Rochester, United States

    4 publications · 128 citations

    4

    h-index

  4. HZ
    Hanwang Zhang

    Nanyang Technological University, Singapore

    4 publications · 421 citations

    4

    h-index

  5. LC
    Long Chen

    Zhejiang University, China

    4 publications · 421 citations

    4

    h-index

1–5 of 172

Venues

  1. arXiv (Cornell University)

    Repository · 19 publications · 1.2K citations

    15

    h-index

  2. 2

    h-index

  3. Image and Vision Computing

    2 publications · 30 citations

    2

    h-index

  4. IEEE Transactions on Multimedia

    2 publications · 123 citations

    2

    h-index

  5. Information Fusion

    2 publications · 235 citations

    2

    h-index

1–5