Xingjian Li
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition top 5%
- Computer Networks and Communications top 10%
- Information Systems top 10%
- Signal Processing top 10%
- Topics
- Domain Adaptation and Few-Shot Learning (12 papers)Advanced Neural Network Applications (12 papers)Multimodal Machine Learning Applications (8 papers)
- Partner nations
- ChinaUnited StatesMacao
In The Last Decade
Xingjian Li
51 papers receiving 765 citations
Hit Papers
Peers
Comparison fields: 5 of 137
- Artificial Intelligence 335
- Computer Vision and Pattern Recognition 175
- Computer Networks and Communications 68
- Information Systems 63
- Signal Processing 53
Countries citing papers authored by Xingjian Li
This map shows the geographic impact of Xingjian Li'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 Xingjian Li with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Xingjian Li more than expected).
Fields of papers citing papers by Xingjian Li
This network shows the impact of papers produced by Xingjian Li. 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 Xingjian Li. The network helps show where Xingjian Li may publish in the future.
Co-authorship network of co-authors of Xingjian Li
This figure shows the co-authorship network connecting the top 25 collaborators of Xingjian Li. A scholar is included among the top collaborators of Xingjian Li 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 Xingjian Li. Xingjian Li 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 | 6 | |
| 5 | 17 | |
| 6 | 5 | |
| 7 | 1 | |
| 8 | 41 | |
| 9 | 19 | |
| 10 | 16 | |
| 11 | 3 | |
| 12 | 2 | |
| 13 | Pay Attention to Features, Transfer Learn faster CNNs | 30 |
| 14 | Delta: Deep Learning Transfer using Feature Map with Attention for Convolutional Networks. | 2 |
| 15 | 18 | |
| 16 | 10 | |
| 17 | Discovering Protein Clusters | 0 |
| 18 | 24 | |
| 19 | Visualization for structured constraint satisfaction problems | 2 |
| 20 | Cluster Graphs as Abstractions for Constraint Satisfaction Problems | 2 |
About Xingjian Li
Xingjian Li is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Hardware and Architecture, having authored 57 papers that have together received 782 indexed citations. Recurring topics across this work include Domain Adaptation and Few-Shot Learning (12 papers), Advanced Neural Network Applications (12 papers) and Multimodal Machine Learning Applications (8 papers). The work is most often cited by research in Health Informatics (15 citations), Artificial Intelligence (335 citations) and Computer Vision and Pattern Recognition (175 citations). Xingjian Li has collaborated with scholars based in China, United States and Macao. Frequent co-authors include Dejing Dou, Haoyi Xiong, Ji Liu, Xuhong Li, Xiao Zhang, Chengzhong Xu, Xuanyu Wu, Jiang Bian, Abulikemu Abuduweili and Humphrey Shi. Their work appears in journals such as ACS Nano, Water Research and Scientific Reports.
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.