Visual Saliency Detection
Visual Saliency Detection is a computational method that identifies the most visually prominent regions or objects in an image by modeling human attention mechanisms to prioritize areas with distinctive low-level features, high-level semantics, or task-driven cues.
1.8K
Publications
171.3K
Citations
4.6K
Authors
1.1K
Institutions
Publications per year
2017–2026
1K
Parents
1.8K
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
Kelvin Xu, Jimmy Ba, Ryan Kiros et al. · arXiv (Cornell University) · 2015 · 7.5K citations · Full text
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
Peter Anderson, Xiaodong He, Chris Buehler et al. · 2018 · 5.1K citations
Saliency Detection: A Spectral Residual Approach
Xiaodi Hou, Liqing Zhang · 2007 · 3.4K citations
1–5 of 1.8K
4.6K
40
h-index
30
h-index
25
h-index
23
h-index
22
h-index
1–5 of 4.6K
1.1K
Dalian University of Technology
Dalian, China
47 h-index
276 publications
49.5K citations

Northwestern Polytechnical University
Xi'an, China
39 h-index
163 publications
19.8K citations

Chinese Academy of Sciences
Beijing, China
37 h-index
169 publications
14.3K citations

Beihang University
Beijing, China
37 h-index
228 publications
15.5K citations
Nanyang Technological University
Singapore, Singapore
37 h-index
113 publications
10.4K citations

Nankai University
Tianjin, China
36 h-index
121 publications
28.1K citations

Shanghai Jiao Tong University
Shanghai, China
30 h-index
146 publications
15.3K citations

Xidian University
Xi'an, China
28 h-index
125 publications
6.1K citations
1–8 of 1.1K
141 publications · 15.9K citations
69
h-index
72 publications · 6K citations
44
h-index
TPAMI · 50 publications · 25.2K citations
41
h-index
60 publications · 3.8K citations
35
h-index
82 publications · 2.8K citations
28
h-index
1–5