Ivor W. Tsang
- Artificial Intelligence top 0.05%
- Computer Vision and Pattern Recognition top 0.05%
- Media Technology top 0.2%
- Computational Mechanics top 1%
- Signal Processing top 0.5%
- Co-authors
- James T. KwokLixin DuanDong XuShenghua GaoLiang-Tien ChiaPak-Ming CheungYew-Soon OngFeiping Nie
- Topics
- Face and Expression Recognition (56 papers)Domain Adaptation and Few-Shot Learning (56 papers)Advanced Image and Video Retrieval Techniques (55 papers)
In The Last Decade
Ivor W. Tsang
265 papers receiving 11.7k citations
Hit Papers
Peers
Comparison fields: 5 of 167
- Artificial Intelligence 7.3k
- Computer Vision and Pattern Recognition 6.8k
- Media Technology 1.2k
- Computational Mechanics 865
- Signal Processing 780
Countries citing papers authored by Ivor W. Tsang
This map shows the geographic impact of Ivor W. Tsang'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 Ivor W. Tsang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ivor W. Tsang more than expected).
Fields of papers citing papers by Ivor W. Tsang
This network shows the impact of papers produced by Ivor W. Tsang. 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 Ivor W. Tsang. The network helps show where Ivor W. Tsang may publish in the future.
Co-authorship network of co-authors of Ivor W. Tsang
This figure shows the co-authorship network connecting the top 25 collaborators of Ivor W. Tsang. A scholar is included among the top collaborators of Ivor W. Tsang 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 Ivor W. Tsang. Ivor W. Tsang 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 | 0 | |
| 3 | 1 | |
| 4 | 2 | |
| 5 | 6 | |
| 6 | 3 | |
| 7 | 2 | |
| 8 | 1 | |
| 9 | 25 | |
| 10 | 4 | |
| 11 | 4 | |
| 12 | 35 | |
| 13 | 33 | |
| 14 | 16 | |
| 15 | 33 | |
| 16 | 31 | |
| 17 | 43 | |
| 18 | 4 | |
| 19 | Riemannian Pursuit for Big Matrix Recovery | 31 |
| 20 | Ensembles of partially trained SWMs with multiplicative updates | 1 |
About Ivor W. Tsang
Ivor W. Tsang is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Computational Mathematics, having authored 280 papers that have together received 12.1k indexed citations. Recurring topics across this work include Face and Expression Recognition (56 papers), Domain Adaptation and Few-Shot Learning (56 papers) and Advanced Image and Video Retrieval Techniques (55 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (6.8k citations), Artificial Intelligence (7.3k citations) and Computational Mathematics (83 citations). Ivor W. Tsang has collaborated with scholars based in Singapore, Australia and China. Frequent co-authors include James T. Kwok, Lixin Duan, Dong Xu, Shenghua Gao, Liang-Tien Chia, Dong Xu, Pak-Ming Cheung, Yew-Soon Ong, Feiping Nie and Mingkui Tan. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Hypertension and IEEE Transactions on Image Processing.
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.