Multi-context Fusion
Multi-Context Fusion is a context-aware recommendation method that integrates multiple contextual dimensions—such as temporal, spatial, social, and situational factors—into a unified representation to enhance recommendation accuracy by modeling their complex interactions and dependencies.
161
Publications
10.2K
Citations
552
Authors
233
Institutions
Publications per year
2017–2026
121
Parents
161
DESIRE: Distant Future Prediction in Dynamic Scenes with Interacting Agents
Namhoon Lee, Wongun Choi, Paul Vernaza et al. · 2017 · 1.1K citations
Multi-context Attention for Human Pose Estimation
Xiao Chu, Wei Yang, Wanli Ouyang et al. · 2017 · 736 citations
Full-Resolution Residual Networks for Semantic Segmentation in Street Scenes
Tobias Pohlen, Alexander Hermans, Markus Mathias et al. · 2017 · 611 citations
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233

Chinese Academy of Sciences
Beijing, China
8 h-index
23 publications
530 citations

University of Macau
Macao, Macao
4 h-index
8 publications
546 citations
University of Chinese Academy of Sciences
Beijing, China
4 h-index
8 publications
213 citations
Tsinghua University
Beijing, China
4 h-index
4 publications
998 citations
Stanford University
Stanford, United States
4 h-index
5 publications
1.3K citations

Xi'an Jiaotong University
Xi'an, China
3 h-index
12 publications
564 citations
Wuhan University
Wuhan, China
3 h-index
8 publications
436 citations

Universidad Autónoma de Madrid
Madrid, Spain
3 h-index
3 publications
590 citations
1–8 of 233
8 publications · 273 citations
10
h-index
Repository · 10 publications · 545 citations
10
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6 publications · 401 citations
6
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Conference · 5 publications · 1.2K citations
5
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4 publications · 105 citations
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