Semi-supervised Semantic Segmentation
Semi-Supervised Semantic Segmentation is a machine learning approach within pixel-level image understanding that leverages both a small set of labeled images and a larger set of unlabeled images to train models, distinguishing itself from fully-supervised methods by reducing reliance on extensive manual annotations while maintaining high segmentation accuracy.
264
Publications
20.3K
Citations
1.1K
Authors
343
Institutions
Publications per year
2017–2026
255
Parents
264
Inf-Net: Automatic COVID-19 Lung Infection Segmentation From CT Images
Deng-Ping Fan, Tao Zhou, Ge-Peng Ji et al. · IEEE Transactions on Medical Imaging · 2020 · 1.2K citations
Semi-Supervised Semantic Segmentation with Cross Pseudo Supervision
Xiaokang Chen, Yuhui Yuan, Gang Zeng et al. · 2021 · 1.1K citations
1–5 of 264
1.1K
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Inception Institute of Artificial Intelligence, United Arab Emirates
6 publications · 1.7K citations
6
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5
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5
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5
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1–5 of 1.1K
343

Chinese Academy of Sciences
Beijing, China
13 h-index
47 publications
1.7K citations

Zhejiang University
Hangzhou, China
9 h-index
22 publications
1.9K citations
University of Chinese Academy of Sciences
Beijing, China
9 h-index
28 publications
1.1K citations
Chinese University of Hong Kong
Hong Kong, Hong Kong
8 h-index
21 publications
2.9K citations
Wuhan University
Wuhan, China
7 h-index
16 publications
1.8K citations

The University of Sydney
Sydney, Australia
6 h-index
12 publications
926 citations

University of Hong Kong
Pok Fu Lam, Hong Kong
6 h-index
11 publications
818 citations
Aerospace Information Research Institute
Beijing, China
6 h-index
22 publications
671 citations
1–8 of 343
Repository · 49 publications · 2.5K citations
24
h-index
12 publications · 419 citations
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11 publications · 362 citations
10
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Conference · 9 publications · 1K citations
9
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TPAMI · 7 publications · 316 citations
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1–5