Hierarchical Feature Aggregation
Hierarchical Feature Aggregation is a feature aggregation method that integrates visual or semantic features across multiple scales or levels of a hierarchy, distinguishing itself by exploiting multi-level representations to preserve both fine-grained details and high-level contextual information in a structured, layered manner.
62
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4.8K
Citations
239
Authors
97
Institutions
Publications per year
2017–2026
55
Parents
62
HyperNet: Towards Accurate Region Proposal Generation and Joint Object Detection
Tao Kong, Anbang Yao, Yurong Chen et al. · 2016 · 844 citations
Residual Feature Aggregation Network for Image Super-Resolution
Jie Liu, Wenjie Zhang, Yuting Tang et al. · 2020 · 615 citations
Cross-level Feature Aggregation Network for Polyp Segmentation
Tao Zhou, Yi Zhou, Kelei He et al. · Pattern Recognition · 2023 · 306 citations
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Chinese Academy of Sciences
Beijing, China
5 h-index
15 publications
1.8K citations
Wuhan University
Wuhan, China
4 h-index
11 publications
285 citations
Dalian University of Technology
Dalian, China
4 h-index
16 publications
912 citations

Nanjing University of Information Science and Technology
Nanjing, China
3 h-index
8 publications
411 citations
Beijing Academy of Artificial Intelligence
Beijing, China
3 h-index
6 publications
283 citations
University of Chinese Academy of Sciences
Beijing, China
3 h-index
7 publications
729 citations
Tsinghua University
Beijing, China
3 h-index
7 publications
2K citations
Peng Cheng Laboratory
Shenzhen, China
3 h-index
4 publications
229 citations
1–8 of 97
Repository · 4 publications · 184 citations
5
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4 publications · 306 citations
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2 publications · 76 citations
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3 publications · 198 citations
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GRSL · 2 publications · 27 citations
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