Visual Concept Learning

Visual Concept Learning is a computational framework that enables machines to acquire and generalize interpretable visual representations by associating image-derived features with human-understandable semantic concepts through structured learning mechanisms.

574

Publications

93.8K

Citations

1.7K

Authors

507

Institutions

Publications per year

2017–2026

264

Authors

1.7K

  1. AA
    Anthony A. Wright

    The University of Texas Health Science Center at Houston, United States

    12 publications · 843 citations

    12

    h-index

  2. JB
    Joshua B. Tenenbaum

    Massachusetts Institute of Technology, United States

    10 publications · 4.2K citations

    10

    h-index

  3. JS
    Jeffrey S. Katz

    Auburn University, United States

    9 publications · 488 citations

    9

    h-index

  4. DP
    Devi Parikh

    Georgia Institute of Technology, United States

    8 publications · 21.8K citations

    8

    h-index

  5. AT
    Antonio Torralba

    Massachusetts Institute of Technology, United States

    8 publications · 762 citations

    8

    h-index

1–5 of 1.7K

Venues

  1. arXiv (Cornell University)

    Repository · 81 publications · 12.4K citations

    33

    h-index

  2. IEEE Transactions on Multimedia

    13 publications · 764 citations

    12

    h-index

  3. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 12 publications · 740 citations

    12

    h-index

  4. IEEE Transactions on Pattern Analysis and Machine Intelligence

    TPAMI · 11 publications · 3.8K citations

    11

    h-index

  5. Neuropsychologia

    6 publications · 528 citations

    6

    h-index

1–5