Sijie Song
- Computer Vision and Pattern Recognition top 0.5%
- Artificial Intelligence top 2%
- Biomedical Engineering top 5%
- Human-Computer Interaction top 1%
- Computational Mechanics
- Topics
- Human Pose and Action Recognition (12 papers)Anomaly Detection Techniques and Applications (6 papers)Generative Adversarial Networks and Image Synthesis (5 papers)
- Journals
- Journal of the American Chemical SocietyIEEE Transactions on Pattern Analysis and Machine IntelligenceIEEE Transactions on Image Processing
- Partner nations
- ChinaUnited StatesFrance
In The Last Decade
Sijie Song
25 papers receiving 1.4k citations
Hit Papers
Peers
Comparison fields: 5 of 109
- Computer Vision and Pattern Recognition 1.2k
- Artificial Intelligence 581
- Biomedical Engineering 555
- Human-Computer Interaction 283
- Computational Mechanics 57
Countries citing papers authored by Sijie Song
This map shows the geographic impact of Sijie 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 Sijie Song with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sijie Song more than expected).
Fields of papers citing papers by Sijie Song
This network shows the impact of papers produced by Sijie 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 Sijie Song. The network helps show where Sijie Song may publish in the future.
Co-authorship network of co-authors of Sijie Song
This figure shows the co-authorship network connecting the top 25 collaborators of Sijie Song. A scholar is included among the top collaborators of Sijie Song 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 Sijie Song. Sijie Song 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 | 0 | |
| 3 | 2 | |
| 4 | 2 | |
| 5 | 17 | |
| 6 | 8 | |
| 7 | 4 | |
| 8 | 16 | |
| 9 | 86 | |
| 10 | 8 | |
| 11 | 12 | |
| 12 | 51 | |
| 13 | 81 | |
| 14 | 27 | |
| 15 | 191 | |
| 16 | 28 | |
| 17 | An End-to-End Spatio-Temporal Attention Model for Human Action Recognition from Skeleton Databreakdown → | 621 |
| 18 | 116 | |
| 19 | 4 | |
| 20 | 1 |
About Sijie Song
Sijie Song is a scholar working on Computer Vision and Pattern Recognition, Museology and Artificial Intelligence, having authored 27 papers that have together received 1.4k indexed citations. Recurring topics across this work include Human Pose and Action Recognition (12 papers), Anomaly Detection Techniques and Applications (6 papers) and Generative Adversarial Networks and Image Synthesis (5 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (1.2k citations), Human-Computer Interaction (283 citations) and Artificial Intelligence (581 citations). Sijie Song has collaborated with scholars based in China, United States and France. Frequent co-authors include Jiaying Liu, Wenjun Zeng, Junliang Xing, Cuiling Lan, Yanghao Li, Chunhui Liu, Yueyu Hu, Tao Mei, Wei Zhang and Zongming Guo. Their work appears in journals such as Journal of the American Chemical Society, IEEE Transactions on Pattern Analysis and Machine Intelligence 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.