Fengwei Zhou
Impact in
-
- Multimodal Machine Learning Applications
- Advanced Neural Network Applications
- Artificial Intelligence top 10%
- Domain Adaptation and Few-Shot Learning
- Adversarial Robustness in Machine Learning
- Anomaly Detection Techniques and Applications
- Machine Learning and Data Classification
- Topic Modeling
Papers in
-
- Domain Adaptation and Few-Shot Learning 5
- Anomaly Detection Techniques and Applications 2
-
- Advanced Neural Network Applications 2
- Image Processing and 3D Reconstruction 1
- Co-authors
- Zhenguo Li (7 shared papers)Lanqing Hong (6 shared papers)Nanyang Ye (3 shared papers)Haoyue Bai (3 shared papers)S.-H. Gary Chan (2 shared papers)Kaican Li (1 shared paper)Jun Zhu (1 shared paper)Han-Jia Ye (1 shared paper)
- Journals
- IEEE Transactions on Knowledge and Data Engineering (1 paper)Mathematical Proceedings of the Cambridge Philosophical Society (1 paper)Processes (1 paper)2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (1 paper)2021 IEEE/CVF International Conference on Computer Vision (ICCV) (2 papers)
In The Last Decade
Fengwei Zhou
8 papers receiving 137 citations
Peers
Comparison fields: 5 of 47
- Computer Vision and Pattern Recognition 69
- Artificial Intelligence 105
- Biophysics 6
- Media Technology 7
- Information Systems 14
Countries citing papers authored by Fengwei Zhou
This map shows the geographic impact of Fengwei Zhou'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 Fengwei Zhou with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Fengwei Zhou more than expected).
Fields of papers citing papers by Fengwei Zhou
This network shows the impact of papers produced by Fengwei Zhou. 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 Fengwei Zhou. The network helps show where Fengwei Zhou may publish in the future.
Co-authors
The 25 scholars most cited alongside Fengwei Zhou, 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 | 36 | |
| 2 | 2021 | 35 | |
| 3 | 2021 | 21 | |
| 4 | 2021 | 18 | |
| 5 | 2020 | 13 | |
| 6 | 2021 | 13 | |
| 7 | 2023 | 2 | |
| 8 | 2023 | 1 | |
| 9 | 2025 | 0 | |
| 10 | 2019 | 0 |
About Fengwei Zhou
Fengwei Zhou is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems, Discrete Mathematics and Combinatorics and Civil and Structural Engineering, having authored 10 papers that have together received 139 indexed citations. Recurring topics across this work include Domain Adaptation and Few-Shot Learning (5 papers), Anomaly Detection Techniques and Applications (2 papers), Advanced Neural Network Applications (2 papers), Advanced Combinatorial Mathematics (1 paper), Advanced Graph Theory Research (1 paper), COVID-19 diagnosis using AI (1 paper), Image Processing and 3D Reconstruction (1 paper) and Virtual Reality Applications and Impacts (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (69 citations), Artificial Intelligence (105 citations), Biophysics (6 citations), Media Technology (7 citations) and Information Systems (14 citations). Fengwei Zhou has collaborated with scholars based in China, Sweden and Hong Kong. Frequent co-authors include Zhenguo Li, Lanqing Hong, Nanyang Ye, Haoyue Bai, S.-H. Gary Chan, Kaican Li, Jun Zhu, Han-Jia Ye, Rui Sun and Sung‐Ho Bae. Their work appears in journals such as IEEE Transactions on Knowledge and Data Engineering, Mathematical Proceedings of the Cambridge Philosophical Society, Processes, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) and 2021 IEEE/CVF International Conference on Computer Vision (ICCV).
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.