Desen Zhou
Impact in
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- Video Surveillance and Tracking Methods
- Human Pose and Action Recognition
- Advanced Neural Network Applications
- Multimodal Machine Learning Applications
- Advanced Image and Video Retrieval Techniques
- Artificial Intelligence top 1%
- Anomaly Detection Techniques and Applications
Papers in
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- Human Pose and Action Recognition 6
- Video Surveillance and Tracking Methods 3
- Advanced Neural Network Applications 3
- Multimodal Machine Learning Applications 2
- Image Enhancement Techniques 1
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- Anomaly Detection Techniques and Applications 4
- Co-authors
- Siqin Chen (1 shared paper)Yingying Zhang (1 shared paper)Yi Ma (1 shared paper)Xuming He (5 shared papers)Bo Wan (2 shared papers)Yongfei Liu (1 shared paper)Rongjie Li (1 shared paper)Jian Wang (3 shared papers)
- Journals
- IEEE Access (2 papers)2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (1 paper)
- Partner nations
- ChinaUnited KingdomBelgium
In The Last Decade
Desen Zhou
9 papers receiving 1.6k citations
Desen Zhou's Hit Papers
Peers
Comparison fields: 5 of 95
- Computer Vision and Pattern Recognition 1.4k
- Artificial Intelligence 949
- Safety, Risk, Reliability and Quality 258
- Transportation 140
- Media Technology 72
Countries citing papers authored by Desen Zhou
This map shows the geographic impact of Desen Zhou'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 Desen Zhou with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Desen Zhou more than expected).
Fields of papers citing papers by Desen Zhou
This network shows the impact of papers produced by Desen Zhou. 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 Desen Zhou. The network helps show where Desen Zhou may publish in the future.
Co-authors
The 17 scholars most cited alongside Desen Zhou, 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 | Single-Image Crowd Counting via Multi-Column Convolutional Neural Network Hit paper breakdown → | 2016 | 1397 |
| 2 | 2019 | 152 | |
| 3 | 2021 | 58 | |
| 4 | 2022 | 46 | |
| 5 | 2023 | 16 | |
| 6 | 2020 | 8 | |
| 7 | 2020 | 7 | |
| 8 | 2021 | 6 | |
| 9 | 2021 | 1 |
About Desen Zhou
Desen Zhou is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Biomedical Engineering, Surgery and Computational Mechanics, having authored 9 papers that have together received 1.7k indexed citations. Recurring topics across this work include Human Pose and Action Recognition (6 papers), Anomaly Detection Techniques and Applications (4 papers), Video Surveillance and Tracking Methods (3 papers), Advanced Neural Network Applications (3 papers), Gait Recognition and Analysis (2 papers), Multimodal Machine Learning Applications (2 papers), 3D Shape Modeling and Analysis (1 paper) and Image Enhancement Techniques (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (1.4k citations), Artificial Intelligence (949 citations), Safety, Risk, Reliability and Quality (258 citations), Transportation (140 citations) and Media Technology (72 citations). Desen Zhou has collaborated with scholars based in China, United Kingdom and Belgium. Frequent co-authors include Siqin Chen, Yingying Zhang, Yi Ma, Xuming He, Bo Wan, Yongfei Liu, Rongjie Li, Jian Wang, Errui Ding and Shidong Wang. Their work appears in journals such as IEEE Access and 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
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.