Few-shot Object Detection

Few-Shot Object Detection is a subfield of object detection in computer vision that focuses on recognizing and localizing novel object categories from a limited number of annotated training examples, typically ranging from one to few instances per class.

159

Publications

11.2K

Citations

621

Authors

158

Institutions

Publications per year

2017–2026

156

Authors

621

  1. TD
    Trevor Darrell

    University of California, Berkeley, United States

    5 publications · 1.3K citations

    5

    h-index

  2. JM
    Jiawei Ma

    Zhejiang A & F University, China

    5 publications · 593 citations

    5

    h-index

  3. FY
    Fisher Yu

    University of California, Berkeley, United States

    4 publications · 1.2K citations

    4

    h-index

  4. JF
    Jiashi Feng

    National University of Singapore, Singapore

    4 publications · 1.2K citations

    4

    h-index

  5. YW
    Yu-Xiong Wang

    Carnegie Mellon University, United States

    4 publications · 483 citations

    4

    h-index

1–5 of 621

Venues

  1. arXiv (Cornell University)

    Repository · 26 publications · 560 citations

    17

    h-index

  2. 11

    h-index

  3. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 8 publications · 647 citations

    8

    h-index

  4. 6

    h-index

  5. Remote Sensing

    5 publications · 169 citations

    5

    h-index

1–5