Shih-En Wei

19 papers receiving 3.4k citations

Hit Papers

OpenPose: Realtime Multi-Person 2D Pose Estimation Using ...2019202620212023201910002.0k3.0k

Peers

Shih-En Wei
Comparison fields: 5 of 151
  • Computer Vision and Pattern Recognition 2.3k
  • Human-Computer Interaction 767
  • Biomedical Engineering 763
  • Artificial Intelligence 522
  • Control and Systems Engineering 384
Replace Zhe Cao with:
Zhe Cao China
Toby Sharp United Kingdom
Gregorij Kurillo United States
Ronan Boulic Switzerland
Tomas Simon United States
Yaser Sheikh United States
Taku Komura United Kingdom
N. Alberto Borghese Italy
Leonid Pishchulin Germany
Hubert P. H. Shum United Kingdom
Shih-En Wei relative to Zhe Cao China Zhe Cao's profile →
Citations per field
00.5×
Zhe Cao · 1×
Citations per year

Countries citing papers authored by Shih-En Wei

Since Specialization
Citations

This map shows the geographic impact of Shih-En Wei'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 Shih-En Wei with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shih-En Wei more than expected).

Fields of papers citing papers by Shih-En Wei

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Shih-En Wei. 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 Shih-En Wei. The network helps show where Shih-En Wei may publish in the future.

Co-authorship network of co-authors of Shih-En Wei

This figure shows the co-authorship network connecting the top 25 collaborators of Shih-En Wei. A scholar is included among the top collaborators of Shih-En Wei 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 Shih-En Wei. Shih-En Wei 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 0
3 74
4 9
5 42
6 1
7 3
8 49
9 80
10 35
11 38
12
OpenPose: Realtime Multi-Person 2D Pose Estimation Using Part Affinity Fieldsbreakdown →
3013
13 94
14 8
15 9
16 16
17 21
18 20
19 8
20 9

About Shih-En Wei

Shih-En Wei is a scholar working on Human-Computer Interaction, Computer Vision and Pattern Recognition and Computer Graphics and Computer-Aided Design, having authored 20 papers that have together received 3.5k indexed citations. Recurring topics across this work include Human Pose and Action Recognition (6 papers), Face recognition and analysis (6 papers) and Advanced Vision and Imaging (5 papers). The work is most often cited by research in Human-Computer Interaction (767 citations), Computer Vision and Pattern Recognition (2.3k citations) and Computer Graphics and Computer-Aided Design (130 citations). Shih-En Wei has collaborated with scholars based in United States, Taiwan and Israel. Frequent co-authors include Tomas Simon, Yaser Sheikh, Zhe Cao, Jason Saragih, Stephen Lombardi, Hung‐Yun Hsieh, Ye Yuan, Kris Kitani, Shunsuke Saito and Shoou-I Yu. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Image Processing and ACM Transactions on Graphics.

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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