Semi-supervised Instance Segmentation
Semi-Supervised Instance Segmentation is an instance segmentation framework that leverages a limited set of fully annotated training samples alongside a larger pool of unlabeled or weakly labeled data to learn pixel-level object masks, distinguishing itself from fully supervised methods by reducing annotation dependency through semi-supervised learning strategies.
73
Publications
4K
Citations
372
Authors
156
Institutions
Publications per year
2017–2026
68
Parents
73
What Makes for Good Views for Contrastive Learning?
Yonglong Tian, Chen Sun, Ben Poole et al. · arXiv (Cornell University) · 2020 · 527 citations · Full text
What Makes for Good Views for Contrastive Learning
Yonglong Tian, Chen Sun, Ben Poole et al. · arXiv (Cornell University) · 2020 · 183 citations · Full text
1–5 of 73
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Institut national de recherche en sciences et technologies du numérique, France
2 publications · 710 citations
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2
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2
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1–5 of 372
156

Chinese Academy of Sciences
Beijing, China
6 h-index
19 publications
580 citations
University of Chinese Academy of Sciences
Beijing, China
5 h-index
9 publications
269 citations

Harbin Institute of Technology
Harbin, China
3 h-index
4 publications
167 citations

Sichuan University
Chengdu, China
3 h-index
13 publications
895 citations

Shenzhen University
Shenzhen, China
3 h-index
6 publications
372 citations

University of Hong Kong
Pok Fu Lam, Hong Kong
3 h-index
8 publications
240 citations
Shenzhen Institutes of Advanced Technology
Shenzhen, China
3 h-index
10 publications
311 citations
Xiamen University
Xiamen, China
3 h-index
9 publications
274 citations
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Repository · 8 publications · 807 citations
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5 publications · 563 citations
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3 publications · 106 citations
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TPAMI · 3 publications · 113 citations
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Conference · 3 publications · 356 citations
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