Weakly Supervised Semantic Segmentation
Weakly Supervised Semantic Segmentation is a semantic segmentation approach that belongs to the category of supervised learning methods but is differentiated by its use of incomplete, limited, or noisy supervision—such as image-level labels, bounding boxes, or scribbles—instead of dense pixel-level annotations for training.
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32.4K
Citations
1.3K
Authors
358
Institutions
Publications per year
2017–2026
335
Parents
371
Simple Does It: Weakly Supervised Instance and Semantic Segmentation
Anna Khoreva, Rodrigo Benenson, Jan Hosang et al. · 2017 · 823 citations
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Chinese Academy of Sciences
Beijing, China
19 h-index
74 publications
7.7K citations
University of Chinese Academy of Sciences
Beijing, China
19 h-index
63 publications
7.5K citations
Tsinghua University
Beijing, China
16 h-index
55 publications
4.4K citations
Tencent (China)
Shenzhen, China
11 h-index
24 publications
1.8K citations
Nanyang Technological University
Singapore, Singapore
11 h-index
30 publications
2.1K citations
ETH Zurich
Zurich, Switzerland
10 h-index
19 publications
2.1K citations

Harbin Institute of Technology
Harbin, China
9 h-index
24 publications
670 citations
Seoul National University
Seoul, South Korea
9 h-index
33 publications
987 citations
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Repository · 68 publications · 2.4K citations
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Conference · 18 publications · 2.5K citations
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TPAMI · 18 publications · 1.8K citations
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Conference · 16 publications · 1.4K citations
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13 publications · 1.7K citations
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