Dit-Yan Yeung
- Artificial Intelligence top 2%
- Computer Vision and Pattern Recognition top 2%
- Signal Processing top 5%
- Computer Networks and Communications top 10%
- Control and Systems Engineering top 10%
- Co-authors
- Tin-Yau KwokYi YangNaiyan WangChuang GanAlexander G. HauptmannWei FanYu ZhangKam-Fai Chan
- Topics
- Face and Expression Recognition (5 papers)Neural Networks and Applications (4 papers)Blind Source Separation Techniques (2 papers)
- Journals
- Pattern RecognitionIEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)Engineering Applications of Artificial Intelligence
- Partner nations
- Hong KongChinaUnited States
In The Last Decade
Dit-Yan Yeung
14 papers receiving 983 citations
Peers
Comparison fields: 5 of 96
- Artificial Intelligence 648
- Computer Vision and Pattern Recognition 469
- Signal Processing 136
- Computer Networks and Communications 125
- Control and Systems Engineering 125
Countries citing papers authored by Dit-Yan Yeung
This map shows the geographic impact of Dit-Yan Yeung'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 Dit-Yan Yeung with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dit-Yan Yeung more than expected).
Fields of papers citing papers by Dit-Yan Yeung
This network shows the impact of papers produced by Dit-Yan Yeung. 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 Dit-Yan Yeung. The network helps show where Dit-Yan Yeung may publish in the future.
Co-authorship network of co-authors of Dit-Yan Yeung
This figure shows the co-authorship network connecting the top 25 collaborators of Dit-Yan Yeung. A scholar is included among the top collaborators of Dit-Yan Yeung 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 Dit-Yan Yeung. Dit-Yan Yeung is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 2 | |
| 3 | 222 | |
| 4 | 28 | |
| 5 | 18 | |
| 6 | 53 | |
| 7 | 14 | |
| 8 | 25 | |
| 9 | 169 | |
| 10 | 2 | |
| 11 | 18 | |
| 12 | 2 | |
| 13 | 157 | |
| 14 | 349 |
About Dit-Yan Yeung
Dit-Yan Yeung is a scholar working on Computer Vision and Pattern Recognition, Computer Graphics and Computer-Aided Design and Artificial Intelligence, having authored 14 papers that have together received 1.1k indexed citations. Recurring topics across this work include Face and Expression Recognition (5 papers), Neural Networks and Applications (4 papers) and Blind Source Separation Techniques (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (469 citations), Artificial Intelligence (648 citations) and Signal Processing (136 citations). Dit-Yan Yeung has collaborated with scholars based in Hong Kong, China and United States. Frequent co-authors include Tin-Yau Kwok, Yi Yang, Naiyan Wang, Chuang Gan, Alexander G. Hauptmann, Wei Fan, Yu Zhang, Kam-Fai Chan, Guang Dai and Yuntao Qian. Their work appears in journals such as Pattern Recognition, IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) and Engineering Applications of 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.