Xingqian Xu
- Computer Vision and Pattern Recognition top 5%
- Media Technology top 5%
- Artificial Intelligence
- Plant Science
- Ecology
- Co-authors
- Humphrey ShiZilong HuangThomas S. HuangNaira HovakimyanYunchao WeiDavid L. WilsonAlexander G. SchwingRóbert Brunner
- Topics
- Generative Adversarial Networks and Image Synthesis (6 papers)Multimodal Machine Learning Applications (3 papers)Advanced Vision and Imaging (2 papers)
- Cited by
- Computer Vision and Pattern RecognitionMedia TechnologyComputer Graphics and Computer-Aided Design
- Journals
- Computer Vision and Image Understanding2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)Institutional Research Information System (Università degli Studi di Trento)
- Partner nations
- United StatesItalyJamaica
In The Last Decade
Xingqian Xu
13 papers receiving 305 citations
Peers
Comparison fields: 5 of 58
- Computer Vision and Pattern Recognition 194
- Media Technology 65
- Artificial Intelligence 59
- Plant Science 54
- Ecology 38
Countries citing papers authored by Xingqian Xu
This map shows the geographic impact of Xingqian 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 Xingqian Xu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Xingqian Xu more than expected).
Fields of papers citing papers by Xingqian Xu
This network shows the impact of papers produced by Xingqian 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 Xingqian Xu. The network helps show where Xingqian Xu may publish in the future.
Co-authorship network of co-authors of Xingqian Xu
This figure shows the co-authorship network connecting the top 25 collaborators of Xingqian Xu. A scholar is included among the top collaborators of Xingqian 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 Xingqian Xu. Xingqian 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 | 7 | |
| 2 | 22 | |
| 3 | 1 | |
| 4 | 0 | |
| 5 | 5 | |
| 6 | 19 | |
| 7 | 1 | |
| 8 | 8 | |
| 9 | 13 | |
| 10 | 28 | |
| 11 | 45 | |
| 12 | 30 | |
| 13 | 139 | |
| 14 | Online robust principal component analysis for background subtraction: a system evaluation on Toyota car data | 3 |
About Xingqian Xu
Xingqian Xu is a scholar working on Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition and Environmental Engineering, having authored 14 papers that have together received 321 indexed citations. Recurring topics across this work include Generative Adversarial Networks and Image Synthesis (6 papers), Multimodal Machine Learning Applications (3 papers) and Advanced Vision and Imaging (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (194 citations), Media Technology (65 citations) and Computer Graphics and Computer-Aided Design (23 citations). Xingqian Xu has collaborated with scholars based in United States, Italy and Jamaica. Frequent co-authors include Humphrey Shi, Zilong Huang, Thomas S. Huang, Naira Hovakimyan, Yunchao Wei, David L. Wilson, Alexander G. Schwing, Róbert Brunner, Mang Tik Chiu and Hrant Khachatrian. Their work appears in journals such as Computer Vision and Image Understanding, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) and Institutional Research Information System (Università degli Studi di Trento).
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.