Open Set Object Detection

Open Set Object Detection is an object detection framework that extends conventional detection paradigms by enabling the identification of both known-class objects and previously unseen object categories during inference, thereby explicitly accounting for unlabeled or unknown instances in the detection process.

27

Publications

2.2K

Citations

126

Authors

42

Institutions

Publications per year

2017–2026

23

Authors

126

  1. ZC
    Zhiguo Cao

    Huazhong University of Science and Technology, China

    3 publications · 238 citations

    3

    h-index

  2. TE
    Terrance E. Boult

    University of Colorado Colorado Springs, United States

    3 publications · 853 citations

    3

    h-index

  3. HX
    Haipeng Xiong

    Huazhong University of Science and Technology, China

    3 publications · 238 citations

    3

    h-index

  4. HL
    Hao Lü

    Huazhong University of Science and Technology, China

    3 publications · 238 citations

    3

    h-index

  5. CL
    Chengxin Liu

    Huazhong University of Science and Technology, China

    3 publications · 238 citations

    3

    h-index

1–5 of 126

Venues

  1. arXiv (Cornell University)

    Repository · 5 publications · 452 citations

    5

    h-index

  2. 4

    h-index

  3. International Journal of Computer Vision

    2 publications · 47 citations

    2

    h-index

  4. Mechanical Systems and Signal Processing

    MSSP · 1 publications · 24 citations

    1

    h-index

  5. 2021 IEEE/CVF International Conference on Computer Vision (ICCV)

    Conference · 1 publications · 56 citations

    1

    h-index

1–5