Intermediate Feature Aggregation

Intermediate Feature Aggregation is a feature fusion methodology that selectively combines mid-level representations across hierarchical layers in deep neural networks to enhance semantic richness while preserving spatial resolution, distinguishing it from early or late aggregation strategies.

13

Publications

746

Citations

59

Authors

27

Institutions

Publications per year

2017–2026

12

Authors

59

  1. YW
    Yaowei Wang

    Peng Cheng Laboratory, China

    1 publications · 60 citations

    1

    h-index

  2. ML
    Min Liu

    Tongji University, China

    1 publications · 17 citations

    1

    h-index

  3. XL
    Xiaoqiang Lu

    Xi'an Institute of Optics and Precision Mechanics, China

    1 publications · 211 citations

    1

    h-index

  4. HD
    Haodong Ding

    Baidu (China), China

    1 publications · 62 citations

    1

    h-index

  5. HV
    Harsha Vardhan Simhadri

    Carnegie Mellon University, United States

    1 publications · 27 citations

    1

    h-index

1–5 of 59

Venues

  1. arXiv (Cornell University)

    Repository · 3 publications · 152 citations

    3

    h-index

  2. IEEE Network

    1 publications · 81 citations

    1

    h-index

  3. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 1 publications · 60 citations

    1

    h-index

  4. 1

    h-index

  5. Neurocomputing

    1 publications · 17 citations

    1

    h-index

1–5