Nanyang Ye
- Artificial Intelligence top 10%
- Computer Vision and Pattern Recognition top 5%
- Electrical and Electronic Engineering
- Media Technology
- Signal Processing
- Co-authors
- Zhanxing ZhuRafał MantiukLanqing HongZhenguo LiHaoyue BaiFengwei ZhouS.-H. Gary ChanChenghu Zhou
- Topics
- Domain Adaptation and Few-Shot Learning (9 papers)Advanced Neural Network Applications (8 papers)Anomaly Detection Techniques and Applications (7 papers)
- Journals
- IEEE Transactions on Pattern Analysis and Machine IntelligenceACM Transactions on GraphicsInternational Journal of Computer Vision
- Partner nations
- ChinaUnited KingdomSweden
In The Last Decade
Nanyang Ye
31 papers receiving 281 citations
Peers
Comparison fields: 5 of 65
- Artificial Intelligence 149
- Computer Vision and Pattern Recognition 147
- Electrical and Electronic Engineering 27
- Media Technology 21
- Signal Processing 16
Countries citing papers authored by Nanyang Ye
This map shows the geographic impact of Nanyang Ye'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 Nanyang Ye with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nanyang Ye more than expected).
Fields of papers citing papers by Nanyang Ye
This network shows the impact of papers produced by Nanyang Ye. 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 Nanyang Ye. The network helps show where Nanyang Ye may publish in the future.
Co-authorship network of co-authors of Nanyang Ye
This figure shows the co-authorship network connecting the top 25 collaborators of Nanyang Ye. A scholar is included among the top collaborators of Nanyang Ye 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 Nanyang Ye. Nanyang Ye 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 | 2 | |
| 3 | 1 | |
| 4 | 2 | |
| 5 | 11 | |
| 6 | 5 | |
| 7 | 0 | |
| 8 | 9 | |
| 9 | 20 | |
| 10 | 3 | |
| 11 | 3 | |
| 12 | 36 | |
| 13 | 6 | |
| 14 | 7 | |
| 15 | 10 | |
| 16 | 7 | |
| 17 | Bayesian Adversarial Learning | 13 |
| 18 | 8 | |
| 19 | 1 | |
| 20 | 1 |
About Nanyang Ye
Nanyang Ye is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Human-Computer Interaction, having authored 35 papers that have together received 292 indexed citations. Recurring topics across this work include Domain Adaptation and Few-Shot Learning (9 papers), Advanced Neural Network Applications (8 papers) and Anomaly Detection Techniques and Applications (7 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (147 citations), Artificial Intelligence (149 citations) and Media Technology (21 citations). Nanyang Ye has collaborated with scholars based in China, United Kingdom and Sweden. Frequent co-authors include Zhanxing Zhu, Rafał Mantiuk, Lanqing Hong, Zhenguo Li, Haoyue Bai, Fengwei Zhou, S.-H. Gary Chan, Chenghu Zhou, Kaican Li and Xiao-Yun Zhou. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, ACM Transactions on Graphics and International Journal of Computer Vision.
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.