Xiaoyang Wu
- Computer Vision and Pattern Recognition top 5%
- Computational Mechanics top 10%
- Environmental Engineering top 10%
- Geology top 5%
- Artificial Intelligence
- Topics
- 3D Shape Modeling and Analysis (8 papers)Advanced Neural Network Applications (6 papers)Computer Graphics and Visualization Techniques (4 papers)
- Journals
- ACS NanoIEEE Transactions on Pattern Analysis and Machine IntelligenceAdvanced Functional Materials
- Partner nations
- ChinaHong KongUnited States
In The Last Decade
Xiaoyang Wu
24 papers receiving 434 citations
Hit Papers
Peers
Comparison fields: 5 of 97
- Computer Vision and Pattern Recognition 183
- Computational Mechanics 124
- Environmental Engineering 81
- Geology 78
- Artificial Intelligence 60
Countries citing papers authored by Xiaoyang Wu
This map shows the geographic impact of Xiaoyang 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 Xiaoyang Wu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Xiaoyang Wu more than expected).
Fields of papers citing papers by Xiaoyang Wu
This network shows the impact of papers produced by Xiaoyang 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 Xiaoyang Wu. The network helps show where Xiaoyang Wu may publish in the future.
Co-authorship network of co-authors of Xiaoyang Wu
This figure shows the co-authorship network connecting the top 25 collaborators of Xiaoyang Wu. A scholar is included among the top collaborators of Xiaoyang 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 Xiaoyang Wu. Xiaoyang 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 | 1 | |
| 2 | 0 | |
| 3 | 0 | |
| 4 | 0 | |
| 5 | 1 | |
| 6 | 0 | |
| 7 | 0 | |
| 8 | 6 | |
| 9 | 4 | |
| 10 | 21 | |
| 11 | 1 | |
| 12 | 2 | |
| 13 | 24 | |
| 14 | 5 | |
| 15 | 14 | |
| 16 | Point Transformer V3: Simpler, Faster, Strongerbreakdown → | 125 |
| 17 | 1 | |
| 18 | 18 | |
| 19 | 9 | |
| 20 | Discussion on the application of aeromagnetic data fractal in predicting in-situ leachable sandstone-type uranium deposits | 1 |
About Xiaoyang Wu
Xiaoyang Wu is a scholar working on Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition and Computational Mechanics, having authored 31 papers that have together received 441 indexed citations. Recurring topics across this work include 3D Shape Modeling and Analysis (8 papers), Advanced Neural Network Applications (6 papers) and Computer Graphics and Visualization Techniques (4 papers). The work is most often cited by research in Computer Graphics and Computer-Aided Design (54 citations), Geology (78 citations) and Computer Vision and Pattern Recognition (183 citations). Xiaoyang Wu has collaborated with scholars based in China, Hong Kong and United States. Frequent co-authors include Hengshuang Zhao, Xihui Liu, Zhuotao Tian, Jiaya Jia, Li Jiang, Tong He, Wanli Ouyang, Peng‐Shuai Wang, Zhijian Liu and Yu Qiao. Their work appears in journals such as ACS Nano, IEEE Transactions on Pattern Analysis and Machine Intelligence and Advanced Functional Materials.
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.