Xiangru Lin
- Computer Vision and Pattern Recognition top 5%
- Artificial Intelligence top 10%
- Media Technology top 10%
- Aerospace Engineering
- Radiology, Nuclear Medicine and Imaging
- Topics
- Advanced Neural Network Applications (7 papers)Domain Adaptation and Few-Shot Learning (5 papers)Multimodal Machine Learning Applications (4 papers)
- Journals
- IEEE Transactions on Pattern Analysis and Machine IntelligenceRare & Special e-Zone (The Hong Kong University of Science and Technology)2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
In The Last Decade
Xiangru Lin
15 papers receiving 441 citations
Peers
Comparison fields: 5 of 57
- Computer Vision and Pattern Recognition 356
- Artificial Intelligence 197
- Media Technology 45
- Aerospace Engineering 37
- Radiology, Nuclear Medicine and Imaging 26
Countries citing papers authored by Xiangru Lin
This map shows the geographic impact of Xiangru 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 Xiangru Lin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Xiangru Lin more than expected).
Fields of papers citing papers by Xiangru Lin
This network shows the impact of papers produced by Xiangru 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 Xiangru Lin. The network helps show where Xiangru Lin may publish in the future.
Co-authorship network of co-authors of Xiangru Lin
This figure shows the co-authorship network connecting the top 25 collaborators of Xiangru Lin. A scholar is included among the top collaborators of Xiangru 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 Xiangru Lin. Xiangru 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 | 7 | |
| 3 | 1 | |
| 4 | 1 | |
| 5 | 36 | |
| 6 | 2 | |
| 7 | 4 | |
| 8 | 36 | |
| 9 | 36 | |
| 10 | 19 | |
| 11 | 15 | |
| 12 | 14 | |
| 13 | 29 | |
| 14 | 44 | |
| 15 | 211 |
About Xiangru Lin
Xiangru Lin is a scholar working on Computer Vision and Pattern Recognition, Computer Graphics and Computer-Aided Design and Artificial Intelligence, having authored 15 papers that have together received 456 indexed citations. Recurring topics across this work include Advanced Neural Network Applications (7 papers), Domain Adaptation and Few-Shot Learning (5 papers) and Multimodal Machine Learning Applications (4 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (356 citations), Artificial Intelligence (197 citations) and Media Technology (45 citations). Xiangru Lin has collaborated with scholars based in China and Hong Kong. Frequent co-authors include Yizhou Yu, Weifeng Ge, Guanbin Li, Xiao Tan, Errui Ding, Zifeng Wu, Jingdong Wang, Junyu Han, Yu Zhang and Mingming Fan. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Rare & Special e-Zone (The Hong Kong University of Science and Technology) and 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
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.