Objectness Scoring

Objectness Scoring is an evaluative mechanism in object detection that quantifies the likelihood of a region proposal containing a meaningful object instance, distinguishing itself from general classification or localization methods by focusing on the presence or absence of salient visual entities independent of specific category labels.

68

Publications

78.2K

Citations

250

Authors

103

Institutions

Publications per year

2017–2026

39

Authors

250

  1. MC
    Ming‐Ming Cheng

    Nankai University, China

    3 publications · 1.4K citations

    3

    h-index

  2. JK
    Juho Kannala

    Aalto University, Finland

    3 publications · 239 citations

    3

    h-index

  3. GZ
    Gao Zhu

    Australian National University, Australia

    3 publications · 276 citations

    3

    h-index

  4. JM
    Jitendra Malik

    University of California, Berkeley, United States

    3 publications · 391 citations

    3

    h-index

  5. HL
    Hongdong Li

    Australian National University, Australia

    3 publications · 276 citations

    3

    h-index

1–5 of 250

Venues

  1. arXiv (Cornell University)

    Repository · 10 publications · 18.5K citations

    10

    h-index

  2. IEEE Transactions on Multimedia

    4 publications · 349 citations

    4

    h-index

  3. 2

    h-index

  4. 2

    h-index

  5. IEEE Transactions on Pattern Analysis and Machine Intelligence

    TPAMI · 2 publications · 54.2K citations

    2

    h-index

1–5