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
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5.2K
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185
Authors
70
Institutions
Publications per year
2017–2026
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Meta-Transfer Learning for Few-Shot Learning
Qianru Sun, Yaoyao Liu, Tat‐Seng Chua et al. · 2019 · 1.3K citations
Dynamic Few-Shot Visual Learning Without Forgetting
Spyros Gidaris, Nikos Komodakis · 2018 · 1.1K citations
Adaptive Subspaces for Few-Shot Learning
Christian Simon, Piotr Koniusz, Richard Nock et al. · 2020 · 451 citations
SimpleShot: Revisiting Nearest-Neighbor Classification for Few-Shot Learning
Yan Wang, Wei‐Lun Chao, Kilian Q. Weinberger et al. · arXiv (Cornell University) · 2019 · 233 citations · Full text
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Tianjin University
Tianjin, China
4 h-index
9 publications
1.7K citations
Tsinghua University
Beijing, China
3 h-index
3 publications
165 citations

Sun Yat-sen University
Guangzhou, China
3 h-index
7 publications
314 citations

Xidian University
Xi'an, China
3 h-index
9 publications
335 citations

National University of Defense Technology
Changsha, China
2 h-index
9 publications
318 citations

Northwestern Polytechnical University
Xi'an, China
2 h-index
3 publications
84 citations

Pennsylvania State University
State College, United States
2 h-index
3 publications
53 citations
Max Planck Institute for Informatics
Saarbrücken, Germany
2 h-index
3 publications
2.6K citations
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Repository · 12 publications · 896 citations
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Conference · 4 publications · 111 citations
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Conference · 3 publications · 326 citations
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1 publications · 145 citations
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2 publications · 86 citations
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