Juan Song
- Human-Computer Interaction top 1%
- Hand Gesture Recognition Systems 9
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- Human Pose and Action Recognition 11
- Advanced Data Compression Techniques 5
- Advanced Image and Video Retrieval Techniques 5
- Image Retrieval and Classification Techniques 4
- Image and Signal Denoising Methods 4
- Neurology top 10%
- Health Informatics top 10%
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- Gait Recognition and Analysis 7
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- Domain Adaptation and Few-Shot Learning 4
Juan Song
70 papers receiving 1.8k citations
Peers
Comparison fields: 5 of 150
- Human-Computer Interaction 347
- Computer Vision and Pattern Recognition 884
- Cardiology and Cardiovascular Medicine 243
- Neurology 77
- Health Informatics 12
Countries citing papers authored by Juan Song
This map shows the geographic impact of Juan Song'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 Juan Song with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Juan Song more than expected).
Fields of papers citing papers by Juan Song
This network shows the impact of papers produced by Juan Song. 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 Juan Song. The network helps show where Juan Song may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Juan Song, 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 | 2025 | 1 | |
| 2 | 2025 | 2 | |
| 3 | 2025 | 0 | |
| 4 | 2024 | 1 | |
| 5 | 2023 | 2 | |
| 6 | 2022 | 27 | |
| 7 | 2022 | 5 | |
| 8 | 2022 | 23 | |
| 9 | 2022 | 3 | |
| 10 | 2021 | 77 | |
| 11 | 2018 | 38 | |
| 12 | 2017 | 202 | |
| 13 | 2016 | 34 | |
| 14 | 2016 | 1 | |
| 15 | 2016 | 18 | |
| 16 | 2016 | 20 | |
| 17 | 2015 | 1 | |
| 18 | [Effect of ischemic postconditioning on the expression of myocardium matrix metalloproteinase-2 induced by ischemia/reperfusion in rats]. | 2014 | 1 |
| 19 | 2008 | 1 | |
| 20 | 2008 | 249 |
About Juan Song
Juan Song is a scholar working on Computer Vision and Pattern Recognition, Human-Computer Interaction, Leadership and Management, Artificial Intelligence and Computational Theory and Mathematics, having authored 72 papers that have together received 1.8k indexed citations. Recurring topics across this work include Human Pose and Action Recognition (11 papers), Hand Gesture Recognition Systems (9 papers), Gait Recognition and Analysis (7 papers), Advanced Data Compression Techniques (5 papers), Advanced Image and Video Retrieval Techniques (5 papers), Image Retrieval and Classification Techniques (4 papers), Domain Adaptation and Few-Shot Learning (4 papers) and Image and Signal Denoising Methods (4 papers). The work is most often cited by research in Human-Computer Interaction (347 citations), Computer Vision and Pattern Recognition (884 citations), Cardiology and Cardiovascular Medicine (243 citations), Neurology (77 citations) and Health Informatics (12 citations). Juan Song has collaborated with scholars based in China, Australia and United States. Frequent co-authors include Peiyi Shen, Guangming Zhu, Liang Zhang, Mohammed Bennamoun, Syed Afaq Ali Shah, Justin Tan, Zhenjun Tang, Xianquan Zhang, Lan S. Chen and Shien‐Fong Lin. Their work appears in journals such as IEEE Access, Neurocomputing, Complex & Intelligent Systems, Journal of Intelligent & Fuzzy Systems and International Journal of Cardiology.
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.