Jun Lei
Impact in
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- Human Pose and Action Recognition
- Handwritten Text Recognition Techniques
- Image Retrieval and Classification Techniques
- Digital Media Forensic Detection
- Media Technology top 10%
- Vehicle License Plate Recognition
Papers in
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- Advanced Image and Video Retrieval Techniques 12
- Image Retrieval and Classification Techniques 6
- Video Surveillance and Tracking Methods 6
- Multimodal Machine Learning Applications 5
- Digital Media Forensic Detection 5
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- Anomaly Detection Techniques and Applications 7
- Co-authors
- Dan Tu (10 shared papers)Qiang Guo (9 shared papers)Shuo Li (11 shared papers)Guohui Li (4 shared papers)Jun Zhang (7 shared papers)Fenglei Wang (4 shared papers)Jun Zhang (5 shared papers)Ming Zhang (2 shared papers)
In The Last Decade
Jun Lei
44 papers receiving 351 citations
Peers
Comparison fields: 5 of 72
- Computer Vision and Pattern Recognition 212
- Media Technology 42
- Artificial Intelligence 133
- Transportation 27
- Information Systems 86
Countries citing papers authored by Jun Lei
This map shows the geographic impact of Jun Lei'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 Jun Lei with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jun Lei more than expected).
Fields of papers citing papers by Jun Lei
This network shows the impact of papers produced by Jun Lei. 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 Jun Lei. The network helps show where Jun Lei may publish in the future.
Co-authors
The 25 scholars most cited alongside Jun Lei, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 46 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2015 | 47 | |
| 2 | 2023 | 46 | |
| 3 | 2016 | 28 | |
| 4 | 2022 | 24 | |
| 5 | 2016 | 23 | |
| 6 | 2022 | 21 | |
| 7 | 2023 | 17 | |
| 8 | 2021 | 16 | |
| 9 | 2018 | 16 | |
| 10 | 2017 | 11 | |
| 11 | 2014 | 9 | |
| 12 | 2017 | 9 | |
| 13 | 2016 | 8 | |
| 14 | 2022 | 7 | |
| 15 | 2021 | 7 | |
| 16 | 2014 | 6 | |
| 17 | 2017 | 5 | |
| 18 | 2016 | 5 | |
| 19 | 2019 | 4 | |
| 20 | 2021 | 4 |
About Jun Lei
Jun Lei is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Information Systems, Management Science and Operations Research and Computer Networks and Communications, having authored 46 papers that have together received 359 indexed citations. Recurring topics across this work include Advanced Image and Video Retrieval Techniques (12 papers), Recommender Systems and Techniques (9 papers), Anomaly Detection Techniques and Applications (7 papers), Image Retrieval and Classification Techniques (6 papers), Video Surveillance and Tracking Methods (6 papers), Multimodal Machine Learning Applications (5 papers), Digital Media Forensic Detection (5 papers) and Advanced Bandit Algorithms Research (4 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (212 citations), Media Technology (42 citations), Artificial Intelligence (133 citations), Transportation (27 citations) and Information Systems (86 citations). Jun Lei has collaborated with scholars based in China and Canada. Frequent co-authors include Dan Tu, Qiang Guo, Shuo Li, Guohui Li, Jun Zhang, Fenglei Wang, Jun Zhang, Ming Zhang, Yifang Qin and Fang Sun. Their work appears in journals such as Applied Sciences, Neurocomputing, IEEE Access, IEEE Transactions on Pattern Analysis and Machine Intelligence and Big Data and Cognitive Computing.
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.