Min-Hung Chen
Impact in
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- Human Pose and Action Recognition
- Multimodal Machine Learning Applications
- Video Surveillance and Tracking Methods
- Advanced Neural Network Applications
- Artificial Intelligence top 10%
- Anomaly Detection Techniques and Applications
- Domain Adaptation and Few-Shot Learning
Papers in
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- Human Pose and Action Recognition 5
- Multimodal Machine Learning Applications 3
- Video Surveillance and Tracking Methods 3
- Image Enhancement Techniques 2
- Advanced Vision and Imaging 2
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- Anomaly Detection Techniques and Applications 4
- Domain Adaptation and Few-Shot Learning 4
- Co-authors
- Ghassan AlRegib (4 shared papers)Zsolt Kira (2 shared papers)Chih‐Yao Ma (1 shared paper)Jian Zheng (1 shared paper)Ruxin Chen (1 shared paper)Dogancan Temel (1 shared paper)Yen‐Yu Lin (3 shared papers)Baopu Li (1 shared paper)
- Journals
- IEEE Transactions on Intelligent Transportation Systems (1 paper)Signal Processing Image Communication (1 paper)ACM Transactions on Multimedia Computing Communications and Applications (1 paper)
- Partner nations
- United StatesTaiwanNetherlands
In The Last Decade
Min-Hung Chen
12 papers receiving 377 citations
Peers
Comparison fields: 5 of 64
- Computer Vision and Pattern Recognition 326
- Artificial Intelligence 195
- Human-Computer Interaction 30
- Media Technology 27
- Biomedical Engineering 67
Countries citing papers authored by Min-Hung Chen
This map shows the geographic impact of Min-Hung Chen'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 Min-Hung Chen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Min-Hung Chen more than expected).
Fields of papers citing papers by Min-Hung Chen
This network shows the impact of papers produced by Min-Hung Chen. 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 Min-Hung Chen. The network helps show where Min-Hung Chen may publish in the future.
Co-authors
The 25 scholars most cited alongside Min-Hung Chen, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2018 | 158 | |
| 2 | 2019 | 111 | |
| 3 | 2019 | 55 | |
| 4 | 2014 | 20 | |
| 5 | 2020 | 15 | |
| 6 | 2023 | 10 | |
| 7 | 2024 | 9 | |
| 8 | 2023 | 7 | |
| 9 | 2023 | 3 | |
| 10 | 2014 | 1 | |
| 11 | 2023 | 1 | |
| 12 | 2025 | 1 | |
| 13 | 2026 | 0 | |
| 14 | 2025 | 0 | |
| 15 | 2025 | 0 |
About Min-Hung Chen
Min-Hung Chen is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Human-Computer Interaction, Surgery and Automotive Engineering, having authored 15 papers that have together received 391 indexed citations. Recurring topics across this work include Human Pose and Action Recognition (5 papers), Anomaly Detection Techniques and Applications (4 papers), Domain Adaptation and Few-Shot Learning (4 papers), Multimodal Machine Learning Applications (3 papers), Video Surveillance and Tracking Methods (3 papers), Hand Gesture Recognition Systems (2 papers), Image Enhancement Techniques (2 papers) and Advanced Vision and Imaging (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (326 citations), Artificial Intelligence (195 citations), Human-Computer Interaction (30 citations), Media Technology (27 citations) and Biomedical Engineering (67 citations). Min-Hung Chen has collaborated with scholars based in United States, Taiwan and Netherlands. Frequent co-authors include Ghassan AlRegib, Zsolt Kira, Chih‐Yao Ma, Jian Zheng, Ruxin Chen, Dogancan Temel, Yen‐Yu Lin, Baopu Li, Yingze Bao and Nick C. Tang. Their work appears in journals such as IEEE Transactions on Intelligent Transportation Systems, Signal Processing Image Communication and ACM Transactions on Multimedia Computing Communications and Applications.
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.