Zhanyu Ma
- Artificial Intelligence top 0.5%
- Computer Vision and Pattern Recognition top 0.5%
- Electrical and Electronic Engineering top 5%
- Signal Processing top 1%
- Computer Networks and Communications top 2%
- Topics
- Domain Adaptation and Few-Shot Learning (57 papers)Advanced Neural Network Applications (38 papers)Multimodal Machine Learning Applications (35 papers)
- Journals
- SHILAP Revista de lepidopterologíaIEEE Transactions on Pattern Analysis and Machine IntelligenceScientific Reports
- Partner nations
- ChinaUnited KingdomSweden
In The Last Decade
Zhanyu Ma
187 papers receiving 4.9k citations
Hit Papers
Peers
Comparison fields: 5 of 153
- Artificial Intelligence 2.4k
- Computer Vision and Pattern Recognition 2.0k
- Electrical and Electronic Engineering 864
- Signal Processing 471
- Computer Networks and Communications 453
Countries citing papers authored by Zhanyu Ma
This map shows the geographic impact of Zhanyu Ma'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 Zhanyu Ma with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Zhanyu Ma more than expected).
Fields of papers citing papers by Zhanyu Ma
This network shows the impact of papers produced by Zhanyu Ma. 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 Zhanyu Ma. The network helps show where Zhanyu Ma may publish in the future.
Co-authorship network of co-authors of Zhanyu Ma
This figure shows the co-authorship network connecting the top 25 collaborators of Zhanyu Ma. A scholar is included among the top collaborators of Zhanyu Ma 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 Zhanyu Ma. Zhanyu Ma is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 0 | |
| 3 | 0 | |
| 4 | 2 | |
| 5 | 2 | |
| 6 | 0 | |
| 7 | 1 | |
| 8 | 4 | |
| 9 | 14 | |
| 10 | 15 | |
| 11 | 1 | |
| 12 | 3 | |
| 13 | 1 | |
| 14 | 1 | |
| 15 | 7 | |
| 16 | ATRM: Attention-based Task-level Relation Module for GNN-based Few-shot Learning. | 1 |
| 17 | 132 | |
| 18 | 41 | |
| 19 | 36 | |
| 20 | 230 |
About Zhanyu Ma
Zhanyu Ma is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Signal Processing, having authored 204 papers that have together received 5.1k indexed citations. Recurring topics across this work include Domain Adaptation and Few-Shot Learning (57 papers), Advanced Neural Network Applications (38 papers) and Multimodal Machine Learning Applications (35 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (2.0k citations), Artificial Intelligence (2.4k citations) and Signal Processing (471 citations). Zhanyu Ma has collaborated with scholars based in China, United Kingdom and Sweden. Frequent co-authors include Jun Guo, Arne Leijon, Xiaoxu Li, Dongliang Chang, Jiyang Xie, Yi-Zhe Song, Jing‐Hao Xue, Qie Sun, Zheng‐Hua Tan and Hong Yu. Their work appears in journals such as SHILAP Revista de lepidopterología, IEEE Transactions on Pattern Analysis and Machine Intelligence and Scientific Reports.
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.