Open-set Recognition
Open-Set Recognition is a paradigm in computer vision that enables a model to correctly identify known classes during inference while simultaneously rejecting samples from unknown classes that were not present during training.
329
Publications
22.2K
Citations
1.1K
Authors
369
Institutions
Publications per year
2017–2026
228
Parents
329
Walter J. Scheirer, Anderson de Rezende Rocha, Amir Sapkota et al. · IEEE Transactions on Pattern Analysis and Machine Intelligence · 2012 · 1.5K citations
Large-Scale Long-Tailed Recognition in an Open World
Ziwei Liu, Zhongqi Miao, Xiaohang Zhan et al. · 2019 · 1.1K citations
1–5 of 329
1.1K
University of Colorado Colorado Springs, United States
8 publications · 2.5K citations
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6
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6
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6
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5
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1–5 of 1.1K
369

Chinese Academy of Sciences
Beijing, China
15 h-index
59 publications
3.2K citations

University of Colorado Colorado Springs
Colorado Springs, United States
9 h-index
21 publications
5K citations
University of Chinese Academy of Sciences
Beijing, China
9 h-index
27 publications
1.1K citations
Tsinghua University
Beijing, China
9 h-index
15 publications
724 citations
Google (United States)
Mountain View, United States
8 h-index
27 publications
3.5K citations

Xi'an Jiaotong University
Xi'an, China
7 h-index
14 publications
717 citations
Nanyang Technological University
Singapore, Singapore
7 h-index
23 publications
3.9K citations

University of Electronic Science and Technology of China
Chengdu, China
7 h-index
29 publications
1K citations
1–8 of 369
TPAMI · 19 publications · 4.8K citations
19
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Repository · 33 publications · 1.5K citations
18
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Repository · 14 publications · 546 citations
13
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JSLHR · 8 publications · 560 citations
8
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JASA · 8 publications · 237 citations
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1–5