Few-shot Object Recognition

Few-Shot Object Recognition is a subfield of object recognition that focuses on the development of models capable of accurately identifying novel object categories from a limited number of labeled training examples per class.

49

Publications

5.2K

Citations

185

Authors

70

Institutions

Publications per year

2017–2026

49

Authors

185

  1. ZW
    Zhirong Wu

    Microsoft Research Asia (China), China

    2 publications · 73 citations

    2

    h-index

  2. AK
    Abhishek Kumar

    Georgia Institute of Technology, United States

    2 publications · 349 citations

    2

    h-index

  3. RF
    Rogério Feris

    IBM (United States), United States

    2 publications · 349 citations

    2

    h-index

  4. NK
    Nikos Komodakis

    École nationale des ponts et chaussées, France

    2 publications · 1.2K citations

    2

    h-index

  5. BS
    Bernt Schiele

    Max Planck Institute for Informatics, Germany

    2 publications · 1.3K citations

    2

    h-index

1–5 of 185

Venues

  1. arXiv (Cornell University)

    Repository · 12 publications · 896 citations

    12

    h-index

  2. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 4 publications · 111 citations

    4

    h-index

  3. 3

    h-index

  4. 2

    h-index

  5. IEEE Transactions on Image Processing

    2 publications · 86 citations

    2

    h-index

1–5