Shi‐Min Hu

285 papers receiving 15.1k citations

Hit Papers

Global contrast based salient region detection20092026201420202011201420222016202350010001.5k2.0k

Peers

Shi‐Min Hu
Comparison fields: 5 of 180
  • Computer Vision and Pattern Recognition 11.3k
  • Computational Mechanics 3.2k
  • Computer Graphics and Computer-Aided Design 2.6k
  • Media Technology 1.6k
  • Aerospace Engineering 1.4k
Replace Niloy J. Mitra with:
Niloy J. Mitra United Kingdom
Heung‐Yeung Shum China
Frédo Durand United States
Jian Sun China
Ayellet Tal Israel
Philip H. S. Torr United Kingdom
Pascal Fua Switzerland
Mubarak Shah United States
Ruigang Yang United States
Carsten Rother United Kingdom
Shi‐Min Hu relative to Niloy J. Mitra United Kingdom Niloy J. Mitra's profile →
Citations per field
00.5×2.6×
Niloy J. Mitra · 1×
Citations per year

Countries citing papers authored by Shi‐Min Hu

Since Specialization
Citations

This map shows the geographic impact of Shi‐Min Hu's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Shi‐Min Hu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shi‐Min Hu more than expected).

Fields of papers citing papers by Shi‐Min Hu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Shi‐Min Hu. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Shi‐Min Hu. The network helps show where Shi‐Min Hu may publish in the future.

Co-authorship network of co-authors of Shi‐Min Hu

This figure shows the co-authorship network connecting the top 25 collaborators of Shi‐Min Hu. A scholar is included among the top collaborators of Shi‐Min Hu based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Shi‐Min Hu. Shi‐Min Hu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
#WorkIndexed citations
1 1
2 5
3 1
4 3
5 1
6 1
7 2
8
Visual attention networkbreakdown →
529
9 10
10 6
11
Beyond Self-Attention: External Attention Using Two Linear Layers for Visual Tasksbreakdown →
431
12 36
13 51
14 7
15 17
16 6
17 9
18 8
19
Testing error handling code in device drivers using characteristic fault injection
9
20
Proceedings of the 2006 ACM symposium on Solid and physical modeling
8

About Shi‐Min Hu

Shi‐Min Hu is a scholar working on Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition and Computational Mechanics, having authored 293 papers that have together received 15.6k indexed citations. Recurring topics across this work include Advanced Vision and Imaging (91 papers), 3D Shape Modeling and Analysis (84 papers) and Computer Graphics and Visualization Techniques (76 papers). The work is most often cited by research in Computer Graphics and Computer-Aided Design (2.6k citations), Computer Vision and Pattern Recognition (11.3k citations) and Sensory Systems (1.1k citations). Shi‐Min Hu has collaborated with scholars based in China, United Kingdom and United States. Frequent co-authors include Ming‐Ming Cheng, Xiaolei Huang, Niloy J. Mitra, Guoxin Zhang, Zheng-Ning Liu, Meng-Hao Guo, Philip H. S. Torr, Tai‐Jiang Mu, Song–Hai Zhang and Yu‐Kun Lai. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Communications of the ACM and IEEE Transactions on Image Processing.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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