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
Parents
13
Do Vision Transformers See Like Convolutional Neural Networks?
Maithra Raghu, Thomas Unterthiner, Simon Kornblith et al. · arXiv (Cornell University) · 2021 · 109 citations · Full text
1–5 of 13
59
1
h-index
1
h-index
1
h-index
1
h-index
1
h-index
1–5 of 59
27
University of Chinese Academy of Sciences
Beijing, China
2 h-index
3 publications
279 citations

Chinese Academy of Sciences
Beijing, China
1 h-index
2 publications
422 citations
Peng Cheng Laboratory
Shenzhen, China
1 h-index
5 publications
300 citations
The Ohio State University
Columbus, United States
1 h-index
3 publications
111 citations
University of Washington
Seattle, United States
1 h-index
1 publications
27 citations

École Nationale Supérieure d'Ingénieurs de Caen
Caen, France
1 h-index
1 publications
25 citations
Technicolor (France)
Paris, France
1 h-index
4 publications
100 citations
Microsoft Research (United Kingdom)
Cambridge, United Kingdom
1 h-index
4 publications
108 citations
1–8 of 27
Repository · 3 publications · 152 citations
3
h-index
1 publications · 81 citations
1
h-index
Conference · 1 publications · 60 citations
1
h-index
1 publications · 211 citations
1
h-index
1 publications · 17 citations
1
h-index
1–5