Semi-supervised Annotation
Semi-Supervised Annotation is a machine learning approach to image annotation that combines a small set of labeled data with a large set of unlabeled data to improve annotation accuracy and efficiency by leveraging both supervised and unsupervised learning mechanisms.
200
Publications
9.2K
Citations
751
Authors
312
Institutions
Publications per year
2017–2026
123
Parents
200
Annotating Object Instances with a Polygon-RNN
Lluís Castrejón, Kaustav Kundu, Raquel Urtasun et al. · 2017 · 372 citations
The second release of the RASP system
Ted Briscoe, John M. Carroll, Rebecca Watson · 2006 · 359 citations · Full text
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312
University of Science and Technology of China
Hefei, China
12 h-index
38 publications
1.6K citations
Microsoft Research Asia (China)
Beijing, China
12 h-index
26 publications
2K citations
National University of Singapore
Singapore, Singapore
8 h-index
19 publications
2.1K citations

Centre National de la Recherche Scientifique
Paris, France
6 h-index
11 publications
382 citations

University of Trento
Trento, Italy
6 h-index
13 publications
1.7K citations
Stanford University
Stanford, United States
6 h-index
19 publications
1.6K citations
The University of Texas at Arlington
Arlington, United States
5 h-index
9 publications
930 citations

University of Electronic Science and Technology of China
Chengdu, China
4 h-index
6 publications
423 citations
1–8 of 312
9 publications · 827 citations
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Repository · 9 publications · 215 citations
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7 publications · 244 citations
8
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6 publications · 233 citations
8
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4 publications · 154 citations
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