Dongyoon Wee
Impact in
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- Video Surveillance and Tracking Methods
- Human Pose and Action Recognition
- Advanced Neural Network Applications
- Advanced Image and Video Retrieval Techniques
- Multimodal Machine Learning Applications
Papers in
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- Human Pose and Action Recognition 6
- Video Surveillance and Tracking Methods 5
- Multimodal Machine Learning Applications 1
- Optical measurement and interference techniques 1
- Image Enhancement Techniques 1
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- Anomaly Detection Techniques and Applications 4
- Co-authors
- Dit‐Yan Yeung (1 shared paper)Myunggu Kang (1 shared paper)Junmo Kim (2 shared papers)Jin-Hyung Kim (1 shared paper)Soonmin Bae (1 shared paper)Sanghoon Lee (1 shared paper)Pilhyeon Lee (1 shared paper)Hyeran Byun (1 shared paper)
- Journals
- Sensors (1 paper)IEEE Robotics and Automation Letters (1 paper)2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) (1 paper)Proceedings of the AAAI Conference on Artificial Intelligence (1 paper)
- Partner nations
- South KoreaCanadaHong Kong
In The Last Decade
Dongyoon Wee
10 papers receiving 95 citations
Peers
Comparison fields: 5 of 36
- Computer Vision and Pattern Recognition 80
- Human-Computer Interaction 6
- Artificial Intelligence 31
- Aerospace Engineering 12
- Safety, Risk, Reliability and Quality 4
Countries citing papers authored by Dongyoon Wee
This map shows the geographic impact of Dongyoon Wee'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 Dongyoon Wee with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dongyoon Wee more than expected).
Fields of papers citing papers by Dongyoon Wee
This network shows the impact of papers produced by Dongyoon Wee. 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 Dongyoon Wee. The network helps show where Dongyoon Wee may publish in the future.
Co-authors
The 20 scholars most cited alongside Dongyoon Wee, 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 | 2023 | 38 | |
| 2 | 2020 | 21 | |
| 3 | 2023 | 15 | |
| 4 | 2021 | 10 | |
| 5 | 2023 | 4 | |
| 6 | 2020 | 3 | |
| 7 | 2023 | 1 | |
| 8 | 2024 | 1 | |
| 9 | 2022 | 1 | |
| 10 | 2023 | 1 | |
| 11 | 2025 | 0 |
About Dongyoon Wee
Dongyoon Wee is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Biomedical Engineering, Atomic and Molecular Physics, and Optics and Safety, Risk, Reliability and Quality, having authored 11 papers that have together received 95 indexed citations. Recurring topics across this work include Human Pose and Action Recognition (6 papers), Video Surveillance and Tracking Methods (5 papers), Anomaly Detection Techniques and Applications (4 papers), Gait Recognition and Analysis (3 papers), Hand Gesture Recognition Systems (1 paper), Multimodal Machine Learning Applications (1 paper), Optical measurement and interference techniques (1 paper) and Image Enhancement Techniques (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (80 citations), Human-Computer Interaction (6 citations), Artificial Intelligence (31 citations), Aerospace Engineering (12 citations) and Safety, Risk, Reliability and Quality (4 citations). Dongyoon Wee has collaborated with scholars based in South Korea, Canada and Hong Kong. Frequent co-authors include Dit‐Yan Yeung, Myunggu Kang, Junmo Kim, Jin-Hyung Kim, Soonmin Bae, Sanghoon Lee, Pilhyeon Lee, Hyeran Byun, Taeoh Kim and Inwoong Lee. Their work appears in journals such as Sensors, IEEE Robotics and Automation Letters, 2023 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.