Minjie Cai
- Computer Vision and Pattern Recognition top 5%
- Human-Computer Interaction top 5%
- Control and Systems Engineering
- Artificial Intelligence
- Signal Processing top 10%
- Co-authors
- Yoichi SatoFeng LuKris KitaniYifei HuangHao ChenYunfei LiuZhenqiang LiXionghu Zhong
- Topics
- Human Pose and Action Recognition (8 papers)Hand Gesture Recognition Systems (5 papers)Visual Attention and Saliency Detection (4 papers)
- Journals
- IEEE Transactions on Pattern Analysis and Machine IntelligenceIEEE Transactions on Image ProcessingIEEE Transactions on Intelligent Transportation Systems
- Partner nations
- ChinaJapanUnited States
In The Last Decade
Minjie Cai
15 papers receiving 259 citations
Peers
Comparison fields: 5 of 48
- Computer Vision and Pattern Recognition 182
- Human-Computer Interaction 114
- Control and Systems Engineering 49
- Artificial Intelligence 49
- Signal Processing 44
Countries citing papers authored by Minjie Cai
This map shows the geographic impact of Minjie Cai'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 Minjie Cai with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Minjie Cai more than expected).
Fields of papers citing papers by Minjie Cai
This network shows the impact of papers produced by Minjie Cai. 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 Minjie Cai. The network helps show where Minjie Cai may publish in the future.
Co-authorship network of co-authors of Minjie Cai
This figure shows the co-authorship network connecting the top 25 collaborators of Minjie Cai. A scholar is included among the top collaborators of Minjie Cai 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 Minjie Cai. Minjie Cai 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 | 2 | |
| 3 | 0 | |
| 4 | 3 | |
| 5 | 7 | |
| 6 | 3 | |
| 7 | 0 | |
| 8 | 28 | |
| 9 | 9 | |
| 10 | 16 | |
| 11 | 34 | |
| 12 | 35 | |
| 13 | 10 | |
| 14 | 18 | |
| 15 | 24 | |
| 16 | 6 | |
| 17 | 39 | |
| 18 | 27 |
About Minjie Cai
Minjie Cai is a scholar working on Human-Computer Interaction, Computer Vision and Pattern Recognition and Signal Processing, having authored 18 papers that have together received 261 indexed citations. Recurring topics across this work include Human Pose and Action Recognition (8 papers), Hand Gesture Recognition Systems (5 papers) and Visual Attention and Saliency Detection (4 papers). The work is most often cited by research in Human-Computer Interaction (114 citations), Computer Vision and Pattern Recognition (182 citations) and Signal Processing (44 citations). Minjie Cai has collaborated with scholars based in China, Japan and United States. Frequent co-authors include Yoichi Sato, Feng Lu, Kris Kitani, Yifei Huang, Hao Chen, Yunfei Liu, Zhenqiang Li, Xionghu Zhong, Wenwu Wang and Yue Gao. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Image Processing and IEEE Transactions on Intelligent Transportation Systems.
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.