Multi-scale Feature Aggregation
Multi-Scale Feature Aggregation is a feature aggregation method that integrates visual representations across multiple spatial scales to enhance discriminative capability and contextual understanding in instance segmentation.
197
Publications
23K
Citations
864
Authors
260
Institutions
Publications per year
2017–2026
197
197
Efficient Multi-Scale Attention Module with Cross-Spatial Learning
Daliang Ouyang, Su He, Guozhong Zhang et al. · 2023 · 1.6K citations · Full text
Fisher Yu, Dequan Wang, Evan Shelhamer et al. · 2018 · 1.6K citations
Omni-Scale Feature Learning for Person Re-Identification
Kaiyang Zhou, Yongxin Yang, Andrea Cavallaro et al. · 2019 · 987 citations
HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation
Bowen Cheng, Bin Xiao, Jingdong Wang et al. · 2020 · 919 citations
1–5 of 197
260

Chinese Academy of Sciences
Beijing, China
11 h-index
37 publications
2.3K citations
University of Chinese Academy of Sciences
Beijing, China
8 h-index
18 publications
856 citations
Wuhan University
Wuhan, China
8 h-index
22 publications
1K citations
Korea Advanced Institute of Science and Technology
Daejeon, South Korea
7 h-index
22 publications
2.5K citations
Nanjing University of Science and Technology
Nanjing, China
6 h-index
22 publications
3.4K citations
Peng Cheng Laboratory
Shenzhen, China
6 h-index
7 publications
406 citations

Beihang University
Beijing, China
6 h-index
19 publications
2.9K citations

Nanjing Forestry University
Nanjing, China
5 h-index
5 publications
246 citations
1–8 of 260
Repository · 26 publications · 1.2K citations
18
h-index
16 publications · 592 citations
13
h-index
11 publications · 833 citations
11
h-index
9 publications · 243 citations
9
h-index
5 publications · 640 citations
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