Ke Xu
- Computer Vision and Pattern Recognition top 0.5%
- Media Technology top 1%
- Artificial Intelligence top 5%
- Automotive Engineering top 5%
- Signal Processing top 10%
- Co-authors
- Rynson W. H. LauXin YangBaocai YinQiang ZhangTianyu WangLizhuang MaXin TanShaohua Guo
- Topics
- Visual Attention and Saliency Detection (12 papers)Advanced Vision and Imaging (12 papers)Advanced Neural Network Applications (11 papers)
- Journals
- Journal of the American Chemical SocietyIEEE Transactions on Pattern Analysis and Machine IntelligenceChemical Communications
- Partner nations
- ChinaHong KongUnited States
In The Last Decade
Ke Xu
71 papers receiving 1.9k citations
Hit Papers
Peers
Comparison fields: 5 of 132
- Computer Vision and Pattern Recognition 1.5k
- Media Technology 408
- Artificial Intelligence 373
- Automotive Engineering 131
- Signal Processing 89
Countries citing papers authored by Ke Xu
This map shows the geographic impact of Ke Xu'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 Ke Xu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ke Xu more than expected).
Fields of papers citing papers by Ke Xu
This network shows the impact of papers produced by Ke Xu. 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 Ke Xu. The network helps show where Ke Xu may publish in the future.
Co-authorship network of co-authors of Ke Xu
This figure shows the co-authorship network connecting the top 25 collaborators of Ke Xu. A scholar is included among the top collaborators of Ke Xu 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 Ke Xu. Ke Xu 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 | 0 | |
| 4 | 0 | |
| 5 | 3 | |
| 6 | 8 | |
| 7 | 1 | |
| 8 | 17 | |
| 9 | 0 | |
| 10 | 20 | |
| 11 | 6 | |
| 12 | 49 | |
| 13 | 9 | |
| 14 | 30 | |
| 15 | 4 | |
| 16 | 21 | |
| 17 | 0 | |
| 18 | Learning to Restore Low-Light Images via Decomposition-and-Enhancementbreakdown → | 278 |
| 19 | 95 | |
| 20 | Active Matting | 8 |
About Ke Xu
Ke Xu is a scholar working on Computer Vision and Pattern Recognition, Computational Mathematics and Computer Graphics and Computer-Aided Design, having authored 88 papers that have together received 2.0k indexed citations. Recurring topics across this work include Visual Attention and Saliency Detection (12 papers), Advanced Vision and Imaging (12 papers) and Advanced Neural Network Applications (11 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (1.5k citations), Media Technology (408 citations) and Artificial Intelligence (373 citations). Ke Xu has collaborated with scholars based in China, Hong Kong and United States. Frequent co-authors include Rynson W. H. Lau, Xin Yang, Baocai Yin, Qiang Zhang, Tianyu Wang, Lizhuang Ma, Xin Tan, Shaohua Guo, Nan Cao and Min Wang. Their work appears in journals such as Journal of the American Chemical Society, IEEE Transactions on Pattern Analysis and Machine Intelligence and Chemical Communications.
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.