Category-aware Instance Segmentation
Category-Aware Instance Segmentation is an instance segmentation paradigm that, as a semantic-aware extension of basic instance segmentation, explicitly incorporates class category information to differentiate object instances not only by spatial location but also by semantic class, thereby enabling fine-grained identification and separation of objects within complex scenes.
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5.8K
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281
Authors
92
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2017–2026
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Manish Kumar, Philip H. S. Torr, Andrew Zisserman · 2005 · 327 citations
Learning to Segment Every Thing
Ronghang Hu, Piotr Dollár, Kaiming He et al. · 2018 · 314 citations
BoxInst: High-Performance Instance Segmentation with Box Annotations
Zhi Tian, Chunhua Shen, Xinlong Wang et al. · 2021 · 311 citations
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Chinese University of Hong Kong
Hong Kong, Hong Kong
5 h-index
13 publications
1.9K citations

Chinese Academy of Sciences
Beijing, China
5 h-index
22 publications
1.5K citations

Beihang University
Beijing, China
5 h-index
15 publications
1.7K citations

Shanghai Jiao Tong University
Shanghai, China
4 h-index
6 publications
204 citations

University of Hong Kong
Pok Fu Lam, Hong Kong
4 h-index
11 publications
632 citations
Tencent (China)
Shenzhen, China
4 h-index
11 publications
1.4K citations
University of California, Berkeley
Berkeley, United States
4 h-index
10 publications
1.8K citations
University of Chinese Academy of Sciences
Beijing, China
3 h-index
8 publications
570 citations
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Repository · 13 publications · 580 citations
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TPAMI · 3 publications · 474 citations
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2 publications · 102 citations
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2 publications · 400 citations
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Conference · 2 publications · 84 citations
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