Sihaeng Lee

1.0k citations
8 papers · 656 indexed · 1 hit paper · h-index 6
Topics
Advanced Vision and Imaging (4 papers)Image Processing Techniques and Applications (3 papers)Advanced Image and Video Retrieval Techniques (2 papers)
Journals
IEEE Access2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)Proceedings of the AAAI Conference on Artificial Intelligence
Partner nations
South KoreaCanada

In The Last Decade

Sihaeng Lee

8 papers receiving 635 citations

Hit Papers

Joint Fine-Tuning in Deep Neural Networks for Facial Expr...20152026201820222015100200300400500

Peers

Sihaeng Lee
Comparison fields: 5 of 61
  • Computer Vision and Pattern Recognition 568
  • Experimental and Cognitive Psychology 448
  • Human-Computer Interaction 62
  • Cognitive Neuroscience 57
  • Artificial Intelligence 56
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S L Happy India
Wissam J. Baddar South Korea
Jingting Li China
Mohan Karnati India
Shuyong Gao China
Siyue Xie China
Zhanpeng Zhang China
Anh Cat Le Ngo Malaysia
Yuanliu Liu China
Qiangchang Wang China
Sihaeng Lee relative to S L Happy India S L Happy's profile →
Citations per field
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Citations per year

Countries citing papers authored by Sihaeng Lee

Since Specialization
Citations

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

Fields of papers citing papers by Sihaeng Lee

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sihaeng Lee. 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 Sihaeng Lee. The network helps show where Sihaeng Lee may publish in the future.

Co-authorship network of co-authors of Sihaeng Lee

This figure shows the co-authorship network connecting the top 25 collaborators of Sihaeng Lee. A scholar is included among the top collaborators of Sihaeng Lee 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 Sihaeng Lee. Sihaeng Lee is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

8 of 8 papers shown
#WorkIndexed citations
1 1
2 14
3 2
4 43
5 10
6 23
7
Joint Fine-Tuning in Deep Neural Networks for Facial Expression Recognitionbreakdown →
521
8 42

About Sihaeng Lee

Sihaeng Lee is a scholar working on Computer Vision and Pattern Recognition, Media Technology and Experimental and Cognitive Psychology, having authored 8 papers that have together received 656 indexed citations. Recurring topics across this work include Advanced Vision and Imaging (4 papers), Image Processing Techniques and Applications (3 papers) and Advanced Image and Video Retrieval Techniques (2 papers). The work is most often cited by research in Experimental and Cognitive Psychology (448 citations), Computer Vision and Pattern Recognition (568 citations) and Human-Computer Interaction (62 citations). Sihaeng Lee has collaborated with scholars based in South Korea and Canada. Frequent co-authors include Junmo Kim, Heechul Jung, Junho Yim, Byungju Kim, Doyeon Kim, Chunghyun Ahn, Injae Lee, Taehoon Kim, Alessandra Sala and Seung Hwan Kim. Their work appears in journals such as IEEE Access, 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) and Proceedings of the AAAI Conference on Artificial Intelligence.

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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