Few-shot Semantic Segmentation
Few-Shot Semantic Segmentation is a specialized paradigm within image segmentation that enables the precise categorization of pixels into predefined classes using only a minimal number of annotated support examples per class, thereby addressing the challenge of generalizing segmentation models under extreme data scarcity.
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9.2K
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425
Authors
151
Institutions
Publications per year
2017–2026
107
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PANet: Few-Shot Image Semantic Segmentation With Prototype Alignment
Kaixin Wang, Jun Hao Liew, Yingtian Zou et al. · 2019 · 1.4K citations
Chi Zhang, Guosheng Lin, Fayao Liu et al. · 2019 · 662 citations · Full text
SegGPT: Towards Segmenting Everything In Context
Xinlong Wang, Xiaosong Zhang, Yue Cao et al. · 2023 · 271 citations
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151
University of Chinese Academy of Sciences
Beijing, China
11 h-index
31 publications
1.8K citations

Chinese Academy of Sciences
Beijing, China
7 h-index
24 publications
2K citations
Nanyang Technological University
Singapore, Singapore
6 h-index
15 publications
2.2K citations
National University of Singapore
Singapore, Singapore
5 h-index
11 publications
7.2K citations
Tsinghua University
Beijing, China
5 h-index
8 publications
420 citations

South China University of Technology
Guangzhou, China
4 h-index
17 publications
772 citations

University of Amsterdam
Amsterdam, The Netherlands
4 h-index
7 publications
782 citations

University of Macau
Macao, Macao
4 h-index
4 publications
289 citations
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Repository · 17 publications · 725 citations
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6 publications · 261 citations
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5 publications · 228 citations
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TPAMI · 4 publications · 865 citations
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Conference · 4 publications · 376 citations
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