Weakly Supervised Object Recognition
Weakly Supervised Object Recognition is an object recognition paradigm that, unlike fully supervised approaches, relies on incomplete, imprecise, or noisy label information—such as image-level labels without bounding box annotations—to learn accurate object detectors or localizers.
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23.3K
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2017–2026
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CutMix: Regularization Strategy to Train Strong Classifiers With Localizable Features
Sangdoo Yun, Dongyoon Han, Sanghyuk Chun et al. · 2019 · 4.7K citations
Caltech-UCSD Birds-200-2011 Dataset
Catherine Wah · CaltechAUTHORS (California Institute of Technology) · 2024 · 3.9K citations · Full text
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University of Chinese Academy of Sciences
Beijing, China
10 h-index
37 publications
4.7K citations
University of Technology Sydney
Sydney, Australia
9 h-index
17 publications
1.7K citations
University of Oxford
Oxford, United Kingdom
6 h-index
8 publications
994 citations
Tencent (China)
Shenzhen, China
6 h-index
11 publications
817 citations

Northwestern Polytechnical University
Xi'an, China
6 h-index
19 publications
1.1K citations
Yonsei University
Seoul, South Korea
6 h-index
11 publications
6.3K citations
National University of Singapore
Singapore, Singapore
6 h-index
11 publications
1.6K citations

Chinese Academy of Sciences
Beijing, China
6 h-index
22 publications
1.5K citations
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Repository · 26 publications · 1.4K citations
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TPAMI · 10 publications · 1.3K citations
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Conference · 4 publications · 169 citations
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