Weakly Supervised Image Classification
Weakly Supervised Image Classification is an image classification paradigm that, unlike fully supervised approaches requiring precise pixel-level or instance-level annotations, relies on coarse-grained or incomplete supervision—such as image-level labels—to train models capable of recognizing or localizing objects within images.
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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
15 h-index
52 publications
6.1K citations

Chinese Academy of Sciences
Beijing, China
14 h-index
46 publications
1.7K citations
University of Oxford
Oxford, United Kingdom
11 h-index
21 publications
20K citations
Tencent (China)
Shenzhen, China
11 h-index
17 publications
1.1K citations
National University of Singapore
Singapore, Singapore
10 h-index
16 publications
2.6K citations
University of Technology Sydney
Sydney, Australia
10 h-index
14 publications
1.9K citations

Sun Yat-sen University
Guangzhou, China
10 h-index
29 publications
2.7K citations
Nanyang Technological University
Singapore, Singapore
9 h-index
18 publications
1.6K citations
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Repository · 43 publications · 8.1K citations
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TPAMI · 13 publications · 2.3K citations
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