Few-Shot Image Classification is an image classification paradigm in which a model is required to recognize new categories from a limited number of labeled examples per class, typically ranging from one to a few instances, thereby emphasizing learning under severe data scarcity constraints.
356
Publications
31.3K
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1.3K
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376
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Publications per year
2017–2026
355
Parents
Publications
356
Dynamic Few-Shot Visual Learning Without Forgetting
Spyros Gidaris, Nikos Komodakis · 2018 · 1.1K citations
Few-Shot Learning via Embedding Adaptation With Set-to-Set Functions
Han-Jia Ye, Hexiang Hu, De‐Chuan Zhan et al. · 2020 · 738 citations
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Institutions
376
Northwestern Polytechnical University
Xi'an, China
18 h-index
84 publications
6.2K citations
Chinese Academy of Sciences
Beijing, China
18 h-index
54 publications
3K citations
Nanjing University
Nanjing, China
12 h-index
36 publications
6K citations
Google (United States)
Mountain View, United States
11 h-index
20 publications
2.9K citations
Xidian University
Xi'an, China
11 h-index
49 publications
2.3K citations
University of Chinese Academy of Sciences
Beijing, China
11 h-index
37 publications
1.9K citations
Beihang University
Beijing, China
8 h-index
24 publications
605 citations
Australian National University
Canberra, Australia
8 h-index
18 publications
1K citations
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- arXiv (Cornell University)
Repository · 60 publications · 10.2K citations
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- IEEE Transactions on Geoscience and Remote Sensing
40 publications · 2.7K citations
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- Proceedings of the AAAI Conference on Artificial Intelligence
Conference · 16 publications · 888 citations
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- Remote Sensing
13 publications · 560 citations
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- 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Conference · 10 publications · 1K citations
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