Part-based Object Recognition

Part-Based Object Recognition is a method in computer vision that identifies objects by decomposing them into constituent parts and modeling the spatial relationships among these parts, distinguishing it from holistic approaches that recognize objects based on global features.

375

Publications

62.6K

Citations

1K

Authors

375

Institutions

Publications per year

2017–2026

105

Authors

1K

  1. RG
    Ross Girshick

    Meta (Israel), Israel

    9 publications · 11.7K citations

    9

    h-index

  2. AY
    Alan Yuille

    Johns Hopkins University, United States

    8 publications · 803 citations

    8

    h-index

  3. PF
    Pedro F. Felzenszwalb

    University of Chicago, United States

    7 publications · 11.4K citations

    7

    h-index

  4. TD
    Trevor Darrell

    University of California, Berkeley, United States

    7 publications · 1.6K citations

    7

    h-index

  5. DR
    Deva Ramanan

    Carnegie Mellon University, United States

    7 publications · 10.6K citations

    7

    h-index

1–5 of 1K

Venues

  1. arXiv (Cornell University)

    Repository · 25 publications · 793 citations

    17

    h-index

  2. IEEE Transactions on Pattern Analysis and Machine Intelligence

    TPAMI · 18 publications · 12.2K citations

    16

    h-index

  3. 9

    h-index

  4. 2009 IEEE Conference on Computer Vision and Pattern Recognition

    Conference · 6 publications · 364 citations

    6

    h-index

  5. Computer Vision and Image Understanding

    CVIU · 6 publications · 728 citations

    6

    h-index

1–5