Ying Wen
- Computer Vision and Pattern Recognition top 1%
- Artificial Intelligence top 2%
- Media Technology top 0.5%
- Information Systems top 2%
- Radiology, Nuclear Medicine and Imaging top 10%
- Co-authors
- Lianghua HeJiafeng LiYue LuPengfei ShiKaren M. von DeneenWeinan ZhangHan CaiJun Wang
- Topics
- Medical Image Segmentation Techniques (19 papers)Face and Expression Recognition (19 papers)Advanced Image and Video Retrieval Techniques (11 papers)
- Partner nations
- ChinaUnited StatesUnited Kingdom
In The Last Decade
Ying Wen
80 papers receiving 2.0k citations
Hit Papers
Peers
Comparison fields: 5 of 147
- Computer Vision and Pattern Recognition 1.0k
- Artificial Intelligence 653
- Media Technology 464
- Information Systems 336
- Radiology, Nuclear Medicine and Imaging 196
Countries citing papers authored by Ying Wen
This map shows the geographic impact of Ying Wen'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 Ying Wen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ying Wen more than expected).
Fields of papers citing papers by Ying Wen
This network shows the impact of papers produced by Ying Wen. 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 Ying Wen. The network helps show where Ying Wen may publish in the future.
Co-authorship network of co-authors of Ying Wen
This figure shows the co-authorship network connecting the top 25 collaborators of Ying Wen. A scholar is included among the top collaborators of Ying Wen 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 Ying Wen. Ying Wen 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 | 3 | |
| 3 | 2 | |
| 4 | 1 | |
| 5 | 6 | |
| 6 | 25 | |
| 7 | SCConv: Spatial and Channel Reconstruction Convolution for Feature Redundancybreakdown → | 321 |
| 8 | 3 | |
| 9 | Towards Unifying Behavioral and Response Diversity for Open-ended Learning in Zero-sum Games | 6 |
| 10 | 12 | |
| 11 | Neural Auto-Curricula in Two-Player Zero-Sum Games | 2 |
| 12 | 19 | |
| 13 | 8 | |
| 14 | 13 | |
| 15 | Multiagent Bidirectionally-Coordinated Nets for Learning to Play StarCraft Combat Games. | 94 |
| 16 | Learning text representation using recurrent convolutional neural network with highway layers | 2 |
| 17 | Product-Based Neural Networks for User Response Predictionbreakdown → | 392 |
| 18 | 14 | |
| 19 | 29 | |
| 20 | 2 |
About Ying Wen
Ying Wen is a scholar working on Computer Vision and Pattern Recognition, Media Technology and Signal Processing, having authored 82 papers that have together received 2.1k indexed citations. Recurring topics across this work include Medical Image Segmentation Techniques (19 papers), Face and Expression Recognition (19 papers) and Advanced Image and Video Retrieval Techniques (11 papers). The work is most often cited by research in Media Technology (464 citations), Computer Vision and Pattern Recognition (1.0k citations) and Artificial Intelligence (653 citations). Ying Wen has collaborated with scholars based in China, United States and United Kingdom. Frequent co-authors include Lianghua He, Jiafeng Li, Yue Lu, Pengfei Shi, Karen M. von Deneen, Weinan Zhang, Han Cai, Jun Wang, Yong Yu and Yanru Qu. Their work appears in journals such as Scientific Reports, Brain Research 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.