Multi-Scale Feature Representation is a feature representation method that captures structural and semantic information at multiple scales by integrating hierarchical or cascaded feature extraction processes, thereby enabling robust characterization of patterns across varying levels of detail.
843
Publications
51.8K
Citations
3.5K
Authors
779
Institutions
Publications per year
2017–2026
746
Parents
Publications
843
Deeply Supervised Salient Object Detection with Short Connections
Qibin Hou, Ming‐Ming Cheng, Xiaowei Hu et al. · 2017 · 1.2K citations
Multi-Scale Progressive Fusion Network for Single Image Deraining
Kui Jiang, Zhongyuan Wang, Peng Yi et al. · 2020 · 752 citations
1–5 of 843
Authors
3.5K
- XB
6
h-index
- MC
6
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- LJ
6
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- JJ
4
h-index
- LSLing Shao
Inception Institute of Artificial Intelligence, United Arab Emirates
4 publications · 1.1K citations
4
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1–5 of 3.5K
Institutions
779
Chinese Academy of Sciences
Beijing, China
25 h-index
100 publications
4.2K citations
University of Chinese Academy of Sciences
Beijing, China
21 h-index
75 publications
2.8K citations
Xidian University
Xi'an, China
19 h-index
93 publications
4.7K citations
Wuhan University
Wuhan, China
18 h-index
83 publications
10.1K citations
Peking University
Beijing, China
18 h-index
63 publications
2.5K citations
University of Science and Technology of China
Hefei, China
15 h-index
46 publications
2.9K citations
Beijing Institute of Technology
Beijing, China
15 h-index
45 publications
2.5K citations
National University of Defense Technology
Changsha, China
14 h-index
55 publications
3.6K citations
1–8 of 779
Venues
- IEEE Access
47 publications · 2.4K citations
27
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- Remote Sensing
39 publications · 1.6K citations
25
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- arXiv (Cornell University)
Repository · 49 publications · 2K citations
24
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- IEEE Transactions on Image Processing
26 publications · 2.7K citations
23
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- IEEE Transactions on Geoscience and Remote Sensing
16 publications · 1.3K citations
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1–5