Yi-Ming Chan

817 citations
32 papers · 495 · h-index 12

Impact in

Papers in

Yi-Ming Chan

31 papers receiving 476 citations

Peers

Yi-Ming Chan
Comparison fields: 5 of 62
  • Computer Vision and Pattern Recognition 367
  • Automotive Engineering 157
  • Media Technology 45
  • Safety, Risk, Reliability and Quality 34
  • Artificial Intelligence 100
Replace Ratnesh Kumar with:
Ratnesh Kumar India
Chao-Ho Chen Taiwan
Carmadi Machbub Indonesia
Haoran Wei United States
Vitaly Ablavsky United States
Felipe Codevilla Brazil
Yifan Yang China
Zhongli Wang China
J. Giebel Germany
Seok-Cheol Kee South Korea
Yi-Ming Chan relative to Ratnesh Kumar India Ratnesh Kumar's profile →
Citations per field
00.5×9.5×
Ratnesh Kumar · 1×
Citations per year

Countries citing papers authored by Yi-Ming Chan

Since Specialization
Citations

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

Fields of papers citing papers by Yi-Ming Chan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Yi-Ming Chan. 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 Yi-Ming Chan. The network helps show where Yi-Ming Chan may publish in the future.

Co-authors

The 23 scholars most cited alongside Yi-Ming Chan, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Yi-Ming Chan Line = papers co-authored together Yi-Ming Chan links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 32 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201289
2 200762
3 201251
4
Compacting, Picking and Growing for Unforgetting Continual Learning
201945
5 201831
6 201529
7 201924
8 202320
9 202019
10 201816
11 201114
12 201912
13 201110
14 20128
15 20147
16 20196
17 20126
18 20096
19 20136
20 20106

About Yi-Ming Chan

Yi-Ming Chan is a scholar working on Computer Vision and Pattern Recognition, Automotive Engineering, Artificial Intelligence, Signal Processing and Molecular Biology, having authored 32 papers that have together received 495 indexed citations. Recurring topics across this work include Video Surveillance and Tracking Methods (16 papers), Advanced Neural Network Applications (14 papers), Autonomous Vehicle Technology and Safety (12 papers), Domain Adaptation and Few-Shot Learning (5 papers), Face recognition and analysis (4 papers), Fire Detection and Safety Systems (3 papers), Image Enhancement Techniques (3 papers) and Anomaly Detection Techniques and Applications (3 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (367 citations), Automotive Engineering (157 citations), Media Technology (45 citations), Safety, Risk, Reliability and Quality (34 citations) and Artificial Intelligence (100 citations). Yi-Ming Chan has collaborated with scholars based in Taiwan, South Korea and Hong Kong. Frequent co-authors include Pei‐Yung Hsiao, Li‐Chen Fu, Jia‐Hong Lee, Shih-Shinh Huang, Chu‐Song Chen, Cheng‐En Wu, Chun‐Chi Chen, Chien‐Hung Chen, Chein‐Hung Chen and Chih‐Yi Chiu. Their work appears in journals such as IEEE Transactions on Intelligent Transportation Systems, BMC Bioinformatics, Japanese Journal of Applied Physics, International Journal of Molecular Sciences and IET Intelligent Transport Systems.

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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