Objectness Detection

Objectness Detection is a computational framework in computer vision that identifies image regions likely to contain meaningful objects, distinguished from other detection approaches by focusing on proposing object-like regions without requiring prior knowledge of specific object categories.

137

Publications

84.4K

Citations

492

Authors

186

Institutions

Publications per year

2017–2026

83

Authors

492

  1. FP
    Fatih Porikli

    Australian National University, Australia

    4 publications · 299 citations

    4

    h-index

  2. MC
    Ming‐Ming Cheng

    Nankai University, China

    3 publications · 1.4K citations

    3

    h-index

  3. JH
    Junwei Han

    Northwestern Polytechnical University, China

    3 publications · 770 citations

    3

    h-index

  4. GC
    Gong Cheng

    Northwestern Polytechnical University, China

    3 publications · 695 citations

    3

    h-index

  5. HL
    Huaping Liu

    Tsinghua University, China

    3 publications · 544 citations

    3

    h-index

1–5 of 492

Venues

  1. arXiv (Cornell University)

    Repository · 19 publications · 19K citations

    13

    h-index

  2. 8

    h-index

  3. IEEE Transactions on Pattern Analysis and Machine Intelligence

    TPAMI · 5 publications · 55.6K citations

    5

    h-index

  4. IEEE Transactions on Image Processing

    4 publications · 272 citations

    4

    h-index

  5. IEEE Transactions on Multimedia

    5 publications · 324 citations

    4

    h-index

1–5