Object-centric Learning

Object-Centric Learning is a machine learning framework that models complex scenes by decomposing them into discrete, interpretable representations of individual objects, thereby enabling relational reasoning and structured scene understanding in contrast to holistic, pixel-based approaches.

159

Publications

16.6K

Citations

599

Authors

149

Institutions

Publications per year

2017–2026

134

Authors

599

  1. JW
    Jiajun Wu

    Massachusetts Institute of Technology, United States

    9 publications · 397 citations

    9

    h-index

  2. JB
    Joshua B. Tenenbaum

    Massachusetts Institute of Technology, United States

    8 publications · 331 citations

    8

    h-index

  3. SL
    Sergey Levine

    University of California, Berkeley, United States

    7 publications · 445 citations

    7

    h-index

  4. AV
    Andrea Vedaldi

    University of Oxford, United Kingdom

    6 publications · 149 citations

    6

    h-index

  5. LF
    Li Fei-Fei

    Stanford University, United States

    6 publications · 5.6K citations

    6

    h-index

1–5 of 599

Venues

  1. arXiv (Cornell University)

    Repository · 58 publications · 2.6K citations

    28

    h-index

  2. 5

    h-index

  3. 3

    h-index

  4. Oxford University Research Archive (ORA) (University of Oxford)

    Repository · 3 publications · 143 citations

    3

    h-index

  5. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 3 publications · 110 citations

    3

    h-index

1–5