Unsupervised Instance Segmentation
Unsupervised Instance Segmentation is an instance segmentation paradigm that, unlike its supervised counterparts, operates without relying on manually annotated ground-truth instance masks during model training, thereby achieving the separation and localization of distinct object instances through unsupervised learning mechanisms.
57
Publications
3.1K
Citations
243
Authors
113
Institutions
Publications per year
2017–2026
51
Parents
57
Unsupervised segmentation of natural images via lossy data compression
Allen Y. Yang, John Wright, Yi Ma et al. · Computer Vision and Image Understanding · 2007 · 514 citations
Cut and Learn for Unsupervised Object Detection and Instance Segmentation
Xudong Wang, Rohit Girdhar, Stella X. Yu et al. · 2023 · 168 citations
1–5 of 57
243
2
h-index
2
h-index
2
h-index
2
h-index
2
h-index
1–5 of 243
113
Ministry of Agriculture and Rural Affairs
Beijing, China
4 h-index
17 publications
605 citations
Northwest A&F University
Yangling, China
3 h-index
12 publications
352 citations

Zhejiang University
Hangzhou, China
2 h-index
5 publications
232 citations
Technical University of Munich
Munich, Germany
2 h-index
4 publications
82 citations
Harvard University
Cambridge, United States
2 h-index
4 publications
316 citations
University of California, Berkeley
Berkeley, United States
2 h-index
4 publications
1.4K citations

The University of Sydney
Sydney, Australia
2 h-index
6 publications
474 citations
University of Southern California
Los Angeles, United States
2 h-index
6 publications
378 citations
1–8 of 113
Repository · 4 publications · 46 citations
4
h-index
3 publications · 153 citations
3
h-index
3 publications · 115 citations
3
h-index
4 publications · 91 citations
3
h-index
Conference · 2 publications · 118 citations
2
h-index
1–5