Jun Cen
Impact in
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- Advanced Neural Network Applications
- Human Pose and Action Recognition
- Multimodal Machine Learning Applications
- Artificial Intelligence top 10%
- Domain Adaptation and Few-Shot Learning
- Anomaly Detection Techniques and Applications
Papers in
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- Advanced Neural Network Applications 10
- Human Pose and Action Recognition 2
- Advanced Image and Video Retrieval Techniques 1
- Multimodal Machine Learning Applications 1
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- Domain Adaptation and Few-Shot Learning 7
- Co-authors
- Junhao Cai (4 shared papers)Michael Yu Wang (4 shared papers)Ming Liu (4 shared papers)Yun Peng (4 shared papers)Shiwei Zhang (4 shared papers)Yingya Zhang (3 shared papers)Xiang Wang (2 shared papers)Deli Zhao (1 shared paper)
- Journals
- IEEE Robotics and Automation Letters (2 papers)International Journal of Computer Vision (1 paper)IEEE Transactions on Pattern Analysis and Machine Intelligence (1 paper)Lecture notes in computer science (1 paper)Proceedings of the AAAI Conference on Artificial Intelligence (1 paper)
In The Last Decade
Jun Cen
15 papers receiving 275 citations
Peers
Comparison fields: 5 of 30
- Computer Vision and Pattern Recognition 178
- Artificial Intelligence 121
- Automotive Engineering 29
- Computational Mechanics 33
- Signal Processing 7
Countries citing papers authored by Jun Cen
This map shows the geographic impact of Jun Cen'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 Jun Cen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jun Cen more than expected).
Fields of papers citing papers by Jun Cen
This network shows the impact of papers produced by Jun Cen. 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 Jun Cen. The network helps show where Jun Cen may publish in the future.
Co-authors
The 25 scholars most cited alongside Jun Cen, 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 | 2021 | 67 | |
| 2 | 2023 | 55 | |
| 3 | 2021 | 29 | |
| 4 | 2023 | 26 | |
| 5 | 2022 | 24 | |
| 6 | 2024 | 21 | |
| 7 | 2023 | 15 | |
| 8 | 2022 | 14 | |
| 9 | 2021 | 13 | |
| 10 | 2023 | 8 | |
| 11 | 2021 | 4 | |
| 12 | 2024 | 3 | |
| 13 | 2024 | 1 | |
| 14 | 2025 | 1 | |
| 15 | 2025 | 1 | |
| 16 | 2023 | 0 | |
| 17 | 2025 | 0 | |
| 18 | 2026 | 0 |
About Jun Cen
Jun Cen is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Automotive Engineering, Computational Mechanics and Control and Systems Engineering, having authored 18 papers that have together received 282 indexed citations. Recurring topics across this work include Advanced Neural Network Applications (10 papers), Domain Adaptation and Few-Shot Learning (7 papers), 3D Shape Modeling and Analysis (4 papers), Autonomous Vehicle Technology and Safety (3 papers), Robot Manipulation and Learning (3 papers), Human Pose and Action Recognition (2 papers), Advanced Image and Video Retrieval Techniques (1 paper) and Multimodal Machine Learning Applications (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (178 citations), Artificial Intelligence (121 citations), Automotive Engineering (29 citations), Computational Mechanics (33 citations) and Signal Processing (7 citations). Jun Cen has collaborated with scholars based in China, Hong Kong and Singapore. Frequent co-authors include Junhao Cai, Michael Yu Wang, Ming Liu, Yun Peng, Shiwei Zhang, Yingya Zhang, Xiang Wang, Deli Zhao, Nong Sang and Changxin Gao. Their work appears in journals such as IEEE Robotics and Automation Letters, International Journal of Computer Vision, IEEE Transactions on Pattern Analysis and Machine Intelligence, Lecture notes in computer science 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.