Feature-based Representation
Feature-Based Representation is a cognitive model of semantic memory that represents concepts as structured collections of perceptual, functional, and categorical features, distinguishing itself from holistic or associative models by emphasizing decompositional attribute-level information as the basis for conceptual knowledge.
1.7K
Publications
137.3K
Citations
6K
Authors
1.4K
Institutions
Publications per year
2017–2026
1.1K
Variants
Feature-based Model Of Semantic Memory
Parents
1.7K
Dual Attention Network for Scene Segmentation
Jun Fu, Jing Liu, Haijie Tian et al. · 2019 · 6.8K citations
Unsupervised Feature Learning via Non-parametric Instance Discrimination
Zhirong Wu, Yuanjun Xiong, Stella X. Yu et al. · 2018 · 3.6K citations
1–5 of 1.7K
6K
University of Electronic Science and Technology of China, China
12 publications · 1.1K citations
12
h-index
10
h-index
7
h-index
7
h-index
7
h-index
1–5 of 6K
1.4K

Chinese Academy of Sciences
Beijing, China
36 h-index
174 publications
56.2K citations
University of Chinese Academy of Sciences
Beijing, China
27 h-index
108 publications
24.6K citations
Tianjin University
Tianjin, China
24 h-index
50 publications
4K citations

Northwestern Polytechnical University
Xi'an, China
23 h-index
98 publications
7.1K citations

University of Electronic Science and Technology of China
Chengdu, China
22 h-index
70 publications
4K citations
Carnegie Mellon University
Pittsburgh, United States
22 h-index
63 publications
9.7K citations
National University of Singapore
Singapore, Singapore
21 h-index
64 publications
6K citations
Nanyang Technological University
Singapore, Singapore
20 h-index
55 publications
4.2K citations
1–8 of 1.4K
Repository · 100 publications · 4.8K citations
35
h-index
31 publications · 2.7K citations
26
h-index
33 publications · 2.4K citations
25
h-index
30 publications · 1.9K citations
24
h-index
43 publications · 1.5K citations
23
h-index
1–5