Jinkun Cao
- Computer Vision and Pattern Recognition top 1%
- Artificial Intelligence top 5%
- Aerospace Engineering top 10%
- Electrical and Electronic Engineering
- Control and Systems Engineering
- Topics
- Video Surveillance and Tracking Methods (5 papers)Human Pose and Action Recognition (3 papers)Advanced Vision and Imaging (2 papers)
- Journals
- IEEE Transactions on Multimedia2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Partner nations
- United StatesChinaHong Kong
In The Last Decade
Jinkun Cao
13 papers receiving 947 citations
Hit Papers
Peers
Comparison fields: 5 of 87
- Computer Vision and Pattern Recognition 691
- Artificial Intelligence 272
- Aerospace Engineering 174
- Electrical and Electronic Engineering 70
- Control and Systems Engineering 62
Countries citing papers authored by Jinkun Cao
This map shows the geographic impact of Jinkun Cao'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 Jinkun Cao with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jinkun Cao more than expected).
Fields of papers citing papers by Jinkun Cao
This network shows the impact of papers produced by Jinkun Cao. 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 Jinkun Cao. The network helps show where Jinkun Cao may publish in the future.
Co-authorship network of co-authors of Jinkun Cao
This figure shows the co-authorship network connecting the top 25 collaborators of Jinkun Cao. A scholar is included among the top collaborators of Jinkun Cao 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 Jinkun Cao. Jinkun Cao is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 4 | |
| 3 | 1 | |
| 4 | 0 | |
| 5 | 0 | |
| 6 | Observation-Centric SORT: Rethinking SORT for Robust Multi-Object Trackingbreakdown → | 407 |
| 7 | 25 | |
| 8 | Deep OC-Sort: Multi-Pedestrian Tracking by Adaptive Re-Identificationbreakdown → | 121 |
| 9 | 14 | |
| 10 | DanceTrack: Multi-Object Tracking in Uniform Appearance and Diverse Motionbreakdown → | 173 |
| 11 | 6 | |
| 12 | 121 | |
| 13 | 86 | |
| 14 | Inverse-Transform AutoEncoder for Anomaly Detection | 24 |
| 15 | 1 |
About Jinkun Cao
Jinkun Cao is a scholar working on Computer Vision and Pattern Recognition, Human-Computer Interaction and Geology, having authored 15 papers that have together received 984 indexed citations. Recurring topics across this work include Video Surveillance and Tracking Methods (5 papers), Human Pose and Action Recognition (3 papers) and Advanced Vision and Imaging (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (691 citations), Artificial Intelligence (272 citations) and Aerospace Engineering (174 citations). Jinkun Cao has collaborated with scholars based in United States, China and Hong Kong. Frequent co-authors include Kris Kitani, Jiangmiao Pang, Rawal Khirodkar, Xinshuo Weng, Cewu Lu, Adnan Ahmad, Song Bai, Yi Jiang, Zehuan Yuan and Ping Luo. Their work appears in journals such as IEEE Transactions on Multimedia and 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
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.