WonJun Moon
Impact in
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- Multimodal Machine Learning Applications
- Advanced Image and Video Retrieval Techniques
- Video Analysis and Summarization
- Advanced Neural Network Applications
- Human Pose and Action Recognition
- Video Surveillance and Tracking Methods
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- Domain Adaptation and Few-Shot Learning
Papers in
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- Multimodal Machine Learning Applications 6
- Advanced Image and Video Retrieval Techniques 4
- Video Analysis and Summarization 3
- Video Surveillance and Tracking Methods 2
- Image Enhancement Techniques 1
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- Domain Adaptation and Few-Shot Learning 3
- Anomaly Detection Techniques and Applications 2
- Journals
- IEEE Transactions on Pattern Analysis and Machine Intelligence (1 paper)Pattern Recognition (1 paper)2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (1 paper)Proceedings of the AAAI Conference on Artificial Intelligence (4 papers)
- Partner nations
- South Korea
In The Last Decade
WonJun Moon
7 papers receiving 150 citations
Peers
Comparison fields: 5 of 37
- Computer Vision and Pattern Recognition 111
- Artificial Intelligence 71
- Media Technology 18
- Signal Processing 6
- Radiology, Nuclear Medicine and Imaging 9
Countries citing papers authored by WonJun Moon
This map shows the geographic impact of WonJun Moon'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 WonJun Moon with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites WonJun Moon more than expected).
Fields of papers citing papers by WonJun Moon
This network shows the impact of papers produced by WonJun Moon. 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 WonJun Moon. The network helps show where WonJun Moon may publish in the future.
Co-authors
The 3 scholars most cited alongside WonJun Moon, 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 | 2022 | 57 | |
| 2 | 2023 | 57 | |
| 3 | 2023 | 23 | |
| 4 | 2024 | 7 | |
| 5 | 2024 | 4 | |
| 6 | 2025 | 2 | |
| 7 | 2023 | 2 | |
| 8 | 2025 | 0 | |
| 9 | 2025 | 0 | |
| 10 | 2025 | 0 |
About WonJun Moon
WonJun Moon is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Radiology, Nuclear Medicine and Imaging, Media Technology and Electrical and Electronic Engineering, having authored 10 papers that have together received 152 indexed citations. Recurring topics across this work include Multimodal Machine Learning Applications (6 papers), Advanced Image and Video Retrieval Techniques (4 papers), Video Analysis and Summarization (3 papers), Domain Adaptation and Few-Shot Learning (3 papers), Anomaly Detection Techniques and Applications (2 papers), Video Surveillance and Tracking Methods (2 papers), Image Enhancement Techniques (1 paper) and Retinal Imaging and Analysis (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (111 citations), Artificial Intelligence (71 citations), Media Technology (18 citations), Signal Processing (6 citations) and Radiology, Nuclear Medicine and Imaging (9 citations). WonJun Moon has collaborated with scholars based in South Korea. Frequent co-authors include Jae‐Pil Heo, Seunggu Kang and Cheol-Ho Cho. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Pattern Recognition, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 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.