Feature Pooling Models
Feature Pooling Models is a class of computational models in visual perception theory that accounts for the loss of object recognition in cluttered environments by averaging or integrating similar features across nearby regions in the visual field, thereby distinguishing itself from other crowding models through its reliance on local feature summation or statistical regularities as the primary mechanism underlying perceptual degradation.
94
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16.7K
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333
Authors
140
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Publications per year
2017–2026
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Path Aggregation Network for Instance Segmentation
Shu Liu, Lu Qi, Haifang Qin et al. · 2018 · 8.8K citations
A Theoretical Analysis of Feature Pooling in Visual Recognition
Y-Lan Boureau, Jean Ponce, Yann LeCun · 2010 · 1.2K citations
Path Aggregation Network for Instance Segmentation
Shu Liu, Lu Qi, Haifang Qin et al. · arXiv (Cornell University) · 2018 · 351 citations · Full text
A Comparison of Pooling Methods for Convolutional Neural Networks
Afia Zafar, Muhammad Aamir, Nazri Mohd Nawi et al. · Applied Sciences · 2022 · 342 citations · Full text
1–5 of 94
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École Polytechnique Fédérale de Lausanne, Switzerland
3 publications · 223 citations
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140

Chinese Academy of Sciences
Beijing, China
8 h-index
29 publications
1.1K citations
Institute of Automation
Beijing, China
6 h-index
24 publications
944 citations
Chinese University of Hong Kong
Hong Kong, Hong Kong
4 h-index
8 publications
27.4K citations

City University of Hong Kong
Hong Kong, Hong Kong
4 h-index
8 publications
257 citations
Tsinghua University
Beijing, China
4 h-index
11 publications
411 citations
University of California, Berkeley
Berkeley, United States
4 h-index
5 publications
575 citations
KU Leuven
Leuven, Belgium
3 h-index
7 publications
1.3K citations
New York University
New York, United States
3 h-index
8 publications
2.4K citations
1–8 of 140
Repository · 8 publications · 1.1K citations
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6 publications · 125 citations
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6 publications · 640 citations
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5 publications · 205 citations
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3 publications · 100 citations
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