Baoyuan Wu
- Computer Vision and Pattern Recognition top 0.5%
- Artificial Intelligence top 0.5%
- Signal Processing top 2%
- Aerospace Engineering top 10%
- Safety, Risk, Reliability and Quality top 5%
- Topics
- Adversarial Robustness in Machine Learning (24 papers)Anomaly Detection Techniques and Applications (16 papers)Face and Expression Recognition (12 papers)
- Journals
- Nature CommunicationsIEEE Transactions on Pattern Analysis and Machine IntelligenceIEEE Transactions on Geoscience and Remote Sensing
- Partner nations
- ChinaUnited StatesHong Kong
In The Last Decade
Baoyuan Wu
66 papers receiving 2.8k citations
Hit Papers
Peers
Comparison fields: 5 of 118
- Computer Vision and Pattern Recognition 2.0k
- Artificial Intelligence 1.5k
- Signal Processing 300
- Aerospace Engineering 168
- Safety, Risk, Reliability and Quality 139
Countries citing papers authored by Baoyuan Wu
This map shows the geographic impact of Baoyuan Wu'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 Wu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Baoyuan Wu more than expected).
Fields of papers citing papers by Baoyuan Wu
This network shows the impact of papers produced by Baoyuan Wu. 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 Wu. The network helps show where Baoyuan Wu may publish in the future.
Co-authorship network of co-authors of Baoyuan Wu
This figure shows the co-authorship network connecting the top 25 collaborators of Baoyuan Wu. A scholar is included among the top collaborators of Baoyuan Wu 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 Wu. Baoyuan Wu 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 | 18 | |
| 3 | 19 | |
| 4 | 14 | |
| 5 | 12 | |
| 6 | 57 | |
| 7 | 40 | |
| 8 | 3 | |
| 9 | 8 | |
| 10 | 31 | |
| 11 | 50 | |
| 12 | 5 | |
| 13 | 2 | |
| 14 | Efficient Black-Box Adversarial Attack Guided by the Distribution of Adversarial Perturbations. | 2 |
| 15 | 40 | |
| 16 | 17 | |
| 17 | 71 | |
| 18 | 36 | |
| 19 | 62 | |
| 20 | 86 |
About Baoyuan Wu
Baoyuan Wu is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Signal Processing, having authored 74 papers that have together received 2.9k indexed citations. Recurring topics across this work include Adversarial Robustness in Machine Learning (24 papers), Anomaly Detection Techniques and Applications (16 papers) and Face and Expression Recognition (12 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (2.0k citations), Artificial Intelligence (1.5k citations) and Signal Processing (300 citations). Baoyuan Wu has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include Wei Liu, Bernard Ghanem, Wenhan Luo, Qiang Ji, Siwei Lyu, Bao-Gang Hu, Yujiu Yang, Chao Ma, Xin Li and Zhenyu He. Their work appears in journals such as Nature Communications, IEEE Transactions on Pattern Analysis and Machine Intelligence and IEEE Transactions on Geoscience and Remote Sensing.
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.