Xing Ji
Impact in
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- Face recognition and analysis
- Face and Expression Recognition
- Video Surveillance and Tracking Methods
- Generative Adversarial Networks and Image Synthesis
- Advanced Image and Video Retrieval Techniques
- Advanced Neural Network Applications
- Signal Processing top 1%
- Biometric Identification and Security
Papers in
- Surgery 2
- Pelvic and Acetabular Injuries 2
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- Pelvic floor disorders treatments 2
- Co-authors
- Zheng Zhou (1 shared paper)Dihong Gong (1 shared paper)Yitong Wang (1 shared paper)Wei Liu (1 shared paper)Zhifeng Li (1 shared paper)Hao Wang (1 shared paper)Jingchao Zhou (1 shared paper)Tianfu Wang (3 shared papers)
- Journals
- ACS Chemical Neuroscience (1 paper)IEEE Transactions on Medical Imaging (1 paper)Pattern Recognition (1 paper)Lecture notes in computer science (1 paper)
- Partner nations
- ChinaHong KongUnited States
In The Last Decade
Xing Ji
5 papers receiving 1.8k citations
Xing Ji's Hit Papers
Peers
Comparison fields: 5 of 108
- Computer Vision and Pattern Recognition 1.4k
- Signal Processing 625
- Artificial Intelligence 554
- Radiology, Nuclear Medicine and Imaging 136
- Media Technology 51
Countries citing papers authored by Xing Ji
This map shows the geographic impact of Xing Ji'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 Xing Ji with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Xing Ji more than expected).
Fields of papers citing papers by Xing Ji
This network shows the impact of papers produced by Xing Ji. 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 Xing Ji. The network helps show where Xing Ji may publish in the future.
Co-authors
The 25 scholars most cited alongside Xing Ji, 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 | CosFace: Large Margin Cosine Loss for Deep Face Recognition Hit paper breakdown → | 2018 | 1718 |
| 2 | 2016 | 108 | |
| 3 | 2016 | 25 | |
| 4 | 2016 | 11 | |
| 5 | 2016 | 3 |
About Xing Ji
Xing Ji is a scholar working on Surgery, Rheumatology, Biological Psychiatry, Pulmonary and Respiratory Medicine and Computer Vision and Pattern Recognition, having authored 5 papers that have together received 1.9k indexed citations. Recurring topics across this work include Pelvic floor disorders treatments (2 papers), Pelvic and Acetabular Injuries (2 papers), Radiomics and Machine Learning in Medical Imaging (1 paper), Alzheimer's disease research and treatments (1 paper), Tryptophan and brain disorders (1 paper), Face recognition and analysis (1 paper), Video Surveillance and Tracking Methods (1 paper) and Urinary Tract Infections Management (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (1.4k citations), Signal Processing (625 citations), Artificial Intelligence (554 citations), Radiology, Nuclear Medicine and Imaging (136 citations) and Media Technology (51 citations). Xing Ji has collaborated with scholars based in China, Hong Kong and United States. Frequent co-authors include Zheng Zhou, Dihong Gong, Yitong Wang, Wei Liu, Zhifeng Li, Hao Wang, Jingchao Zhou, Tianfu Wang, Jie‐Zhi Cheng and Dong Ni. Their work appears in journals such as ACS Chemical Neuroscience, IEEE Transactions on Medical Imaging, Pattern Recognition and Lecture notes in computer science.
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.