Sheng Jin
- Computer Vision and Pattern Recognition top 5%
- Artificial Intelligence top 5%
- Aerospace Engineering
- Radiology, Nuclear Medicine and Imaging
- Signal Processing
- Co-authors
- Shijian LuJiaxing HuangXian‐Sheng HuaZhihang FuZhaowei ChengZhihong ChenHongxun YaoXinyu Jin
- Topics
- Advanced Image and Video Retrieval Techniques (8 papers)Multimodal Machine Learning Applications (4 papers)Advanced Neural Network Applications (4 papers)
- Journals
- IEEE Transactions on Pattern Analysis and Machine IntelligenceIEEE Transactions on Image ProcessingNeurocomputing
- Partner nations
- ChinaUnited StatesHong Kong
In The Last Decade
Sheng Jin
15 papers receiving 563 citations
Hit Papers
Peers
Comparison fields: 5 of 89
- Computer Vision and Pattern Recognition 327
- Artificial Intelligence 235
- Aerospace Engineering 41
- Radiology, Nuclear Medicine and Imaging 37
- Signal Processing 35
Countries citing papers authored by Sheng Jin
This map shows the geographic impact of Sheng Jin'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 Sheng Jin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sheng Jin more than expected).
Fields of papers citing papers by Sheng Jin
This network shows the impact of papers produced by Sheng Jin. 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 Sheng Jin. The network helps show where Sheng Jin may publish in the future.
Co-authorship network of co-authors of Sheng Jin
This figure shows the co-authorship network connecting the top 25 collaborators of Sheng Jin. A scholar is included among the top collaborators of Sheng Jin 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 Sheng Jin. Sheng Jin is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 8 | |
| 2 | Vision-Language Models for Vision Tasks: A Surveybreakdown → | 272 |
| 3 | 16 | |
| 4 | 0 | |
| 5 | 10 | |
| 6 | 15 | |
| 7 | 4 | |
| 8 | 3 | |
| 9 | 23 | |
| 10 | 49 | |
| 11 | 133 | |
| 12 | 21 | |
| 13 | 5 | |
| 14 | 5 | |
| 15 | 7 | |
| 16 | Wideband Measurement Technique Based on Dechirp Processing for Radar | 2 |
About Sheng Jin
Sheng Jin is a scholar working on Computer Vision and Pattern Recognition, Cancer Research and Aerospace Engineering, having authored 16 papers that have together received 573 indexed citations. Recurring topics across this work include Advanced Image and Video Retrieval Techniques (8 papers), Multimodal Machine Learning Applications (4 papers) and Advanced Neural Network Applications (4 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (327 citations), Artificial Intelligence (235 citations) and Health Informatics (7 citations). Sheng Jin has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include Shijian Lu, Jiaxing Huang, Xian‐Sheng Hua, Zhihang Fu, Zhaowei Cheng, Zhihong Chen, Hongxun Yao, Xinyu Jin, Chao Chen and Xiaoshuai Sun. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Image Processing and Neurocomputing.
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.