Junting Pan
Impact in
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- Visual Attention and Saliency Detection
- Human Pose and Action Recognition
- Image and Video Quality Assessment
- Video Surveillance and Tracking Methods
- Advanced Image and Video Retrieval Techniques
- Advanced Neural Network Applications
- Multimodal Machine Learning Applications
- Human-Computer Interaction top 5%
Papers in
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- Human Pose and Action Recognition 2
- Image and Signal Denoising Methods 1
- Multimodal Machine Learning Applications 1
- Visual Attention and Saliency Detection 1
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- Intelligent Tutoring Systems and Adaptive Learning 2
- Natural Language Processing Techniques 1
- Co-authors
- Noel E. O’Connor (1 shared paper)Elisa Sayrol (1 shared paper)Xavier Giró-i-Nieto (1 shared paper)Kevin McGuinness (1 shared paper)Hongsheng Li (4 shared papers)Jing Shao (2 shared papers)Siyu Chen (1 shared paper)Mike Zheng Shou (1 shared paper)
- Journals
- International Journal of Computer Vision (1 paper)Energy Reports (1 paper)QRU Quaderns de Recerca en Urbanisme (1 paper)
- Partner nations
- ChinaHong KongUnited States
In The Last Decade
Junting Pan
5 papers receiving 389 citations
Peers
Comparison fields: 5 of 61
- Computer Vision and Pattern Recognition 328
- Human-Computer Interaction 55
- Sensory Systems 38
- Cognitive Neuroscience 68
- Artificial Intelligence 93
Countries citing papers authored by Junting Pan
This map shows the geographic impact of Junting Pan'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 Junting Pan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Junting Pan more than expected).
Fields of papers citing papers by Junting Pan
This network shows the impact of papers produced by Junting Pan. 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 Junting Pan. The network helps show where Junting Pan may publish in the future.
Co-authors
The 25 scholars most cited alongside Junting Pan, 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 | 2016 | 265 | |
| 2 | 2021 | 92 | |
| 3 | 2020 | 29 | |
| 4 | 2020 | 5 | |
| 5 | 2024 | 2 | |
| 6 | 2025 | 0 | |
| 7 | 2023 | 0 | |
| 8 | 2025 | 0 | |
| 9 | 2024 | 0 |
About Junting Pan
Junting Pan is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Computational Theory and Mathematics, Safety Research and Management Science and Operations Research, having authored 9 papers that have together received 393 indexed citations. Recurring topics across this work include Intelligent Tutoring Systems and Adaptive Learning (2 papers), Human Pose and Action Recognition (2 papers), Educational Tools and Methods (1 paper), Natural Language Processing Techniques (1 paper), Software Testing and Debugging Techniques (1 paper), Image and Signal Denoising Methods (1 paper), Multimodal Machine Learning Applications (1 paper) and Visual Attention and Saliency Detection (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (328 citations), Human-Computer Interaction (55 citations), Sensory Systems (38 citations), Cognitive Neuroscience (68 citations) and Artificial Intelligence (93 citations). Junting Pan has collaborated with scholars based in China, Hong Kong and United States. Frequent co-authors include Noel E. O’Connor, Elisa Sayrol, Xavier Giró-i-Nieto, Kevin McGuinness, Hongsheng Li, Jing Shao, Siyu Chen, Mike Zheng Shou, Liu Yu and Bo Chen. Their work appears in journals such as International Journal of Computer Vision, Energy Reports and QRU Quaderns de Recerca en Urbanisme.
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.