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

Publications

4.8K

Citations

239

Authors

97

Institutions

Publications per year

2017–2026

55

Authors

239

  1. PZ
    Pingping Zhang

    Dalian University of Technology, China

    3 publications · 215 citations

    3

    h-index

  2. HL
    Huchuan Lu

    Dalian University of Technology, China

    3 publications · 215 citations

    3

    h-index

  3. CL
    Chenglong Li

    Anhui University, China

    3 publications · 290 citations

    3

    h-index

  4. BL
    Bin Luo

    Anhui University, China

    2 publications · 205 citations

    2

    h-index

  5. LZ
    Liqiang Zhang

    Beijing Normal University, China

    2 publications · 40 citations

    2

    h-index

1–5 of 239

Venues

  1. arXiv (Cornell University)

    Repository · 4 publications · 184 citations

    5

    h-index

  2. 4

    h-index

  3. Remote Sensing

    2 publications · 76 citations

    3

    h-index

  4. 3

    h-index

  5. IEEE Geoscience and Remote Sensing Letters

    GRSL · 2 publications · 27 citations

    2

    h-index

1–5