Weakly Supervised Object Detection
Weakly Supervised Object Detection is an object detection paradigm that belongs to the broader class of supervised learning methods but is distinguished by its reliance on incomplete or coarse-grained supervision, such as image-level labels instead of precise bounding box annotations, to localize and recognize objects within images.
358
Publications
30.6K
Citations
1.1K
Authors
345
Institutions
Publications per year
2017–2026
310
Parents
358
CutMix: Regularization Strategy to Train Strong Classifiers With Localizable Features
Sangdoo Yun, Dongyoon Han, Sanghyuk Chun et al. · 2019 · 4.7K citations
1–5 of 358
1.1K
19
h-index
15
h-index
13
h-index
12
h-index
11
h-index
1–5 of 1.1K
345

Northwestern Polytechnical University
Xi'an, China
25 h-index
108 publications
10.9K citations
University of Chinese Academy of Sciences
Beijing, China
18 h-index
65 publications
7.8K citations

Chinese Academy of Sciences
Beijing, China
17 h-index
60 publications
3.4K citations
Tencent (China)
Shenzhen, China
14 h-index
31 publications
2K citations
University of Technology Sydney
Sydney, Australia
13 h-index
23 publications
2.6K citations
Peng Cheng Laboratory
Shenzhen, China
13 h-index
13 publications
1.1K citations
Xiamen University
Xiamen, China
11 h-index
38 publications
1.9K citations
National University of Singapore
Singapore, Singapore
10 h-index
28 publications
3.5K citations
1–8 of 345
Repository · 47 publications · 2K citations
22
h-index
TPAMI · 20 publications · 2.3K citations
18
h-index
19 publications · 1.7K citations
16
h-index
Conference · 14 publications · 550 citations
14
h-index
12 publications · 1.7K citations
12
h-index
1–5