Feiyu Chen
- Computer Vision and Pattern Recognition top 10%
- Artificial Intelligence top 10%
- Experimental and Cognitive Psychology
- Electrical and Electronic Engineering
- Information Systems
- Co-authors
- Jie ShaoHeng Tao ShenDeqiang OuyangShuyuan ZhuYongxin GeSheng HuangXueliang LiuGeyong Min
- Topics
- Human Pose and Action Recognition (8 papers)Anomaly Detection Techniques and Applications (7 papers)Multimodal Machine Learning Applications (6 papers)
- Cited by
- Computer Vision and Pattern RecognitionExperimental and Cognitive PsychologyArtificial Intelligence
- Journals
- Scientific ReportsIEEE Transactions on CyberneticsIEEE Transactions on Neural Networks and Learning Systems
- Partner nations
- ChinaUnited KingdomUnited States
In The Last Decade
Feiyu Chen
28 papers receiving 254 citations
Peers
Comparison fields: 5 of 49
- Computer Vision and Pattern Recognition 122
- Artificial Intelligence 107
- Experimental and Cognitive Psychology 52
- Electrical and Electronic Engineering 40
- Information Systems 20
Countries citing papers authored by Feiyu Chen
This map shows the geographic impact of Feiyu 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 Feiyu Chen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Feiyu Chen more than expected).
Fields of papers citing papers by Feiyu Chen
This network shows the impact of papers produced by Feiyu 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 Feiyu Chen. The network helps show where Feiyu Chen may publish in the future.
Co-authorship network of co-authors of Feiyu Chen
This figure shows the co-authorship network connecting the top 25 collaborators of Feiyu Chen. A scholar is included among the top collaborators of Feiyu Chen based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Feiyu Chen. Feiyu Chen is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 6 | |
| 3 | 2 | |
| 4 | 0 | |
| 5 | 0 | |
| 6 | 5 | |
| 7 | 7 | |
| 8 | 5 | |
| 9 | 1 | |
| 10 | 29 | |
| 11 | 20 | |
| 12 | 11 | |
| 13 | 3 | |
| 14 | 8 | |
| 15 | 1 | |
| 16 | 18 | |
| 17 | 12 | |
| 18 | 1 | |
| 19 | Crowd motion segmentation algorithm based on video particle flow and FTLE field | 1 |
| 20 | 2 |
About Feiyu Chen
Feiyu Chen is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Experimental and Cognitive Psychology, having authored 31 papers that have together received 260 indexed citations. Recurring topics across this work include Human Pose and Action Recognition (8 papers), Anomaly Detection Techniques and Applications (7 papers) and Multimodal Machine Learning Applications (6 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (122 citations), Experimental and Cognitive Psychology (52 citations) and Artificial Intelligence (107 citations). Feiyu Chen has collaborated with scholars based in China, United Kingdom and United States. Frequent co-authors include Jie Shao, Heng Tao Shen, Deqiang Ouyang, Shuyuan Zhu, Yongxin Ge, Sheng Huang, Xueliang Liu, Geyong Min, Zhiwei Zhao and Hai-Tao Zheng. Their work appears in journals such as Scientific Reports, IEEE Transactions on Cybernetics and IEEE Transactions on Neural Networks and Learning 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.