Kwan-Yee Lin
- Computer Vision and Pattern Recognition top 2%
- Computational Mechanics top 10%
- Media Technology top 5%
- Aerospace Engineering
- Computer Graphics and Computer-Aided Design top 5%
- Co-authors
- Guanxiang WangHongsheng LiXiaogang WangChen QianQian ChenWentao LiuLiang LinXiaokang Chen
- Topics
- Advanced Vision and Imaging (8 papers)Human Pose and Action Recognition (7 papers)Computer Graphics and Visualization Techniques (7 papers)
- Journals
- IEEE Transactions on Pattern Analysis and Machine Intelligence2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)The HKU Scholars Hub (University of Hong Kong)
- Partner nations
- ChinaHong KongUnited States
In The Last Decade
Kwan-Yee Lin
18 papers receiving 584 citations
Peers
Comparison fields: 5 of 54
- Computer Vision and Pattern Recognition 502
- Computational Mechanics 144
- Media Technology 94
- Aerospace Engineering 83
- Computer Graphics and Computer-Aided Design 76
Countries citing papers authored by Kwan-Yee Lin
This map shows the geographic impact of Kwan-Yee Lin'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 Kwan-Yee Lin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kwan-Yee Lin more than expected).
Fields of papers citing papers by Kwan-Yee Lin
This network shows the impact of papers produced by Kwan-Yee Lin. 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 Kwan-Yee Lin. The network helps show where Kwan-Yee Lin may publish in the future.
Co-authorship network of co-authors of Kwan-Yee Lin
This figure shows the co-authorship network connecting the top 25 collaborators of Kwan-Yee Lin. A scholar is included among the top collaborators of Kwan-Yee Lin 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 Kwan-Yee Lin. Kwan-Yee Lin 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 | 11 | |
| 3 | 6 | |
| 4 | 25 | |
| 5 | 4 | |
| 6 | 19 | |
| 7 | 6 | |
| 8 | 32 | |
| 9 | 36 | |
| 10 | 49 | |
| 11 | 43 | |
| 12 | 14 | |
| 13 | 77 | |
| 14 | 73 | |
| 15 | 32 | |
| 16 | 11 | |
| 17 | Self-supervised Deep Multiple Choice Learning Network for Blind Image Quality Assessment. | 2 |
| 18 | 157 |
About Kwan-Yee Lin
Kwan-Yee Lin is a scholar working on Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition and Computational Mechanics, having authored 18 papers that have together received 598 indexed citations. Recurring topics across this work include Advanced Vision and Imaging (8 papers), Human Pose and Action Recognition (7 papers) and Computer Graphics and Visualization Techniques (7 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (502 citations), Computer Graphics and Computer-Aided Design (76 citations) and Geology (72 citations). Kwan-Yee Lin has collaborated with scholars based in China, Hong Kong and United States. Frequent co-authors include Guanxiang Wang, Hongsheng Li, Xiaogang Wang, Chen Qian, Qian Chen, Wentao Liu, Liang Lin, Xiaokang Chen, Gang Zeng and Xipeng Chen. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) and The HKU Scholars Hub (University of Hong Kong).
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.