Baoyuan Wang
- Computer Vision and Pattern Recognition top 0.5%
- Atomic and Molecular Physics, and Optics top 10%
- Media Technology top 2%
- Artificial Intelligence top 10%
- Computational Mechanics top 10%
- Topics
- Generative Adversarial Networks and Image Synthesis (9 papers)Face recognition and analysis (9 papers)Image Enhancement Techniques (9 papers)
- Cited by
- Computer Vision and Pattern RecognitionMedia TechnologyComputer Graphics and Computer-Aided Design
- Journals
- ImmunityIEEE Transactions on Pattern Analysis and Machine IntelligenceIEEE Transactions on Image Processing
- Partner nations
- ChinaUnited StatesHong Kong
In The Last Decade
Baoyuan Wang
46 papers receiving 1.5k citations
Peers
Comparison fields: 5 of 92
- Computer Vision and Pattern Recognition 1.3k
- Atomic and Molecular Physics, and Optics 234
- Media Technology 220
- Artificial Intelligence 175
- Computational Mechanics 103
Countries citing papers authored by Baoyuan Wang
This map shows the geographic impact of Baoyuan 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 Baoyuan Wang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Baoyuan Wang more than expected).
Fields of papers citing papers by Baoyuan Wang
This network shows the impact of papers produced by Baoyuan 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 Baoyuan Wang. The network helps show where Baoyuan Wang may publish in the future.
Co-authorship network of co-authors of Baoyuan Wang
This figure shows the co-authorship network connecting the top 25 collaborators of Baoyuan Wang. A scholar is included among the top collaborators of Baoyuan 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 Baoyuan Wang. Baoyuan 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 | 3 | |
| 2 | 6 | |
| 3 | 7 | |
| 4 | 3 | |
| 5 | 1 | |
| 6 | 4 | |
| 7 | 4 | |
| 8 | 25 | |
| 9 | 1 | |
| 10 | 8 | |
| 11 | 5 | |
| 12 | 19 | |
| 13 | 10 | |
| 14 | 2 | |
| 15 | 1 | |
| 16 | 0 | |
| 17 | 67 | |
| 18 | Maximal Sparsity with Deep Networks | 39 |
| 19 | Max-Margin Multiple-Instance Dictionary Learning | 78 |
| 20 | Parallel H-Tree Based Data Cubing on Graphics Processors | 1 |
About Baoyuan Wang
Baoyuan Wang is a scholar working on Computer Vision and Pattern Recognition, Computer Graphics and Computer-Aided Design and Signal Processing, having authored 48 papers that have together received 1.6k indexed citations. Recurring topics across this work include Generative Adversarial Networks and Image Synthesis (9 papers), Face recognition and analysis (9 papers) and Image Enhancement Techniques (9 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (1.3k citations), Media Technology (220 citations) and Computer Graphics and Computer-Aided Design (74 citations). Baoyuan Wang has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include Yizhou Yu, Yuanming Hu, Yingqing Xu, Stephen Lin, Stephen Lin, Tien‐Tsin Wong, Chenxi Xu, Hao He, Zhicheng Yan and Hao Zhang. Their work appears in journals such as Immunity, IEEE Transactions on Pattern Analysis and Machine Intelligence and IEEE Transactions on Image Processing.
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.