Daoye Wang
Impact in
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- Advanced Neural Network Applications
- Generative Adversarial Networks and Image Synthesis
- Face recognition and analysis
- Advanced Vision and Imaging
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- Computer Graphics and Visualization Techniques
Papers in
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- Advanced Vision and Imaging 4
- Face recognition and analysis 3
- Generative Adversarial Networks and Image Synthesis 2
- Advanced Neural Network Applications 1
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- 3D Shape Modeling and Analysis 5
- Co-authors
- Jimmy Ren (3 shared papers)Rynson W. H. Lau (1 shared paper)Qiong Yan (1 shared paper)Zhanghan Ke (1 shared paper)Paulo Gotardo (2 shared papers)Prashanth Chandran (1 shared paper)Derek Bradley (1 shared paper)Kripasindhu Sarkar (3 shared papers)
- Journals
- IEEE Transactions on Circuits and Systems for Video Technology (1 paper)ACM Transactions on Graphics (1 paper)Computer Networks (1 paper)Computer Graphics Forum (1 paper)
- Partner nations
- SwitzerlandUnited StatesChina
In The Last Decade
Daoye Wang
9 papers receiving 230 citations
Peers
Comparison fields: 5 of 44
- Computer Vision and Pattern Recognition 174
- Computer Graphics and Computer-Aided Design 26
- Artificial Intelligence 111
- Computational Mechanics 42
- Radiology, Nuclear Medicine and Imaging 37
Countries citing papers authored by Daoye Wang
This map shows the geographic impact of Daoye 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 Daoye Wang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daoye Wang more than expected).
Fields of papers citing papers by Daoye Wang
This network shows the impact of papers produced by Daoye 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 Daoye Wang. The network helps show where Daoye Wang may publish in the future.
Co-authors
The 25 scholars most cited alongside Daoye Wang, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2019 | 156 | |
| 2 | 2022 | 35 | |
| 3 | 2021 | 15 | |
| 4 | 2023 | 11 | |
| 5 | 2024 | 8 | |
| 6 | 2023 | 6 | |
| 7 | 2021 | 4 | |
| 8 | 2024 | 2 | |
| 9 | 2024 | 1 |
About Daoye Wang
Daoye Wang is a scholar working on Computer Vision and Pattern Recognition, Computational Mechanics, Computer Graphics and Computer-Aided Design, Computer Networks and Communications and Artificial Intelligence, having authored 9 papers that have together received 238 indexed citations. Recurring topics across this work include 3D Shape Modeling and Analysis (5 papers), Advanced Vision and Imaging (4 papers), Face recognition and analysis (3 papers), Generative Adversarial Networks and Image Synthesis (2 papers), Computer Graphics and Visualization Techniques (2 papers), Advanced Neural Network Applications (1 paper), Domain Adaptation and Few-Shot Learning (1 paper) and Advanced X-ray and CT Imaging (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (174 citations), Computer Graphics and Computer-Aided Design (26 citations), Artificial Intelligence (111 citations), Computational Mechanics (42 citations) and Radiology, Nuclear Medicine and Imaging (37 citations). Daoye Wang has collaborated with scholars based in Switzerland, United States and China. Frequent co-authors include Jimmy Ren, Rynson W. H. Lau, Qiong Yan, Zhanghan Ke, Paulo Gotardo, Prashanth Chandran, Derek Bradley, Kripasindhu Sarkar, Thabo Beeler and Sergio Orts‐Escolano. Their work appears in journals such as IEEE Transactions on Circuits and Systems for Video Technology, ACM Transactions on Graphics, Computer Networks and Computer Graphics Forum.
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.