Lijuan Wang
- Computer Vision and Pattern Recognition top 2%
- Artificial Intelligence top 5%
- Signal Processing top 5%
- Information Systems top 10%
- Control and Systems Engineering top 10%
- Topics
- Advanced Clustering Algorithms Research (10 papers)Face and Expression Recognition (8 papers)Complex Network Analysis Techniques (6 papers)
- Partner nations
- ChinaUnited KingdomUnited States
In The Last Decade
Lijuan Wang
44 papers receiving 869 citations
Peers
Comparison fields: 5 of 122
- Computer Vision and Pattern Recognition 398
- Artificial Intelligence 363
- Signal Processing 133
- Information Systems 71
- Control and Systems Engineering 67
Countries citing papers authored by Lijuan Wang
This map shows the geographic impact of Lijuan Wang'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 Lijuan Wang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Lijuan Wang more than expected).
Fields of papers citing papers by Lijuan Wang
This network shows the impact of papers produced by Lijuan Wang. 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 Lijuan Wang. The network helps show where Lijuan Wang may publish in the future.
Co-authorship network of co-authors of Lijuan Wang
This figure shows the co-authorship network connecting the top 25 collaborators of Lijuan Wang. A scholar is included among the top collaborators of Lijuan Wang 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 Lijuan Wang. Lijuan Wang 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 | 0 | |
| 3 | 6 | |
| 4 | 4 | |
| 5 | Node classification method in social network based on graph encoder network | 0 |
| 6 | 5 | |
| 7 | Double-Head RCNN: Rethinking Classification and Localization for Object Detection | 4 |
| 8 | Rethinking Classification and Localization in R-CNN | 7 |
| 9 | [Neural mechanisms of visual selective attention]. | 1 |
| 10 | 26 | |
| 11 | Health risk assessment of the cultured oyster along the southern coast of Fujian province in China. | 1 |
| 12 | 6 | |
| 13 | A New Method of Brain White Matter Segmentation | 1 |
| 14 | 7 | |
| 15 | Text Driven 3D Photo-Realistic Talking Head. | 25 |
| 16 | Planning Optimization of the Urban Rail Transit Network Based on Improved Grey Fixed Weight Clustering | 1 |
| 17 | 25 | |
| 18 | 61 | |
| 19 | 10 | |
| 20 | A Survey of Text Mining | 3 |
About Lijuan Wang
Lijuan Wang is a scholar working on Computer Vision and Pattern Recognition, Health Informatics and Artificial Intelligence, having authored 46 papers that have together received 910 indexed citations. Recurring topics across this work include Advanced Clustering Algorithms Research (10 papers), Face and Expression Recognition (8 papers) and Complex Network Analysis Techniques (6 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (398 citations), Signal Processing (133 citations) and Artificial Intelligence (363 citations). Lijuan Wang has collaborated with scholars based in China, United Kingdom and United States. Frequent co-authors include Xizhao Wang, Yadong Wang, Shifei Ding, Yanru Wang, Frank K. Soong, Xiao Xu, Ruilian Yu, Gongren Hu, Lei Xie and Bo Fan. Their work appears in journals such as Chemical Engineering Journal, IEEE Access and Information Sciences.
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.