Visual Relationship Detection

Visual Relationship Detection is a computational task in computer vision that identifies and classifies structured triplet relationships—comprising a subject, predicate, and object—within an image, distinguishing it from general object detection by explicitly modeling semantic interactions between visual entities.

205

Publications

24.2K

Citations

638

Authors

213

Institutions

Publications per year

2017–2026

170

Authors

638

  1. TC
    Tat‐Seng Chua

    National University of Singapore, Singapore

    9 publications · 968 citations

    9

    h-index

  2. XS
    Xindi Shang

    National University of Singapore, Singapore

    7 publications · 331 citations

    7

    h-index

  3. LF
    Li Fei-Fei

    Stanford University, United States

    5 publications · 5.7K citations

    5

    h-index

  4. HT
    Heng Tao Shen

    University of Electronic Science and Technology of China, China

    5 publications · 262 citations

    5

    h-index

  5. RK
    Ranjay Krishna

    Stanford University, United States

    5 publications · 5.7K citations

    5

    h-index

1–5 of 638

Venues

  1. arXiv (Cornell University)

    Repository · 37 publications · 3.6K citations

    20

    h-index

  2. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 8 publications · 468 citations

    9

    h-index

  3. IEEE Transactions on Multimedia

    6 publications · 493 citations

    6

    h-index

  4. Pattern Recognition

    4 publications · 271 citations

    5

    h-index

  5. 5

    h-index

1–5