Zimo Liu
- Computer Vision and Pattern Recognition top 2%
- Biomedical Engineering
- Artificial Intelligence
- Safety, Risk, Reliability and Quality
- Ocean Engineering
- Co-authors
- Huchuan LuJingya WangShang GaoDong WangDacheng TaoShaogang GongXiang RuanMing–Hsuan Yang
- Topics
- Video Surveillance and Tracking Methods (5 papers)Human Pose and Action Recognition (4 papers)Gait Recognition and Analysis (2 papers)
- Cited by
- Computer Vision and Pattern RecognitionBiomedical EngineeringSafety, Risk, Reliability and Quality
- Journals
- IEEE Transactions on Neural Networks and Learning SystemsQueen Mary Research Online (Queen Mary University of London)Proceedings of the AAAI Conference on Artificial Intelligence
- Partner nations
- ChinaAustraliaUnited Kingdom
In The Last Decade
Zimo Liu
6 papers receiving 389 citations
Peers
Comparison fields: 5 of 44
- Computer Vision and Pattern Recognition 364
- Biomedical Engineering 159
- Artificial Intelligence 57
- Safety, Risk, Reliability and Quality 22
- Ocean Engineering 13
Countries citing papers authored by Zimo Liu
This map shows the geographic impact of Zimo Liu'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 Zimo Liu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Zimo Liu more than expected).
Fields of papers citing papers by Zimo Liu
This network shows the impact of papers produced by Zimo Liu. 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 Zimo Liu. The network helps show where Zimo Liu may publish in the future.
Co-authorship network of co-authors of Zimo Liu
This figure shows the co-authorship network connecting the top 25 collaborators of Zimo Liu. A scholar is included among the top collaborators of Zimo Liu 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 Zimo Liu. Zimo Liu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 9 | |
| 3 | 185 | |
| 4 | 63 | |
| 5 | 13 | |
| 6 | 125 |
About Zimo Liu
Zimo Liu is a scholar working on Computer Vision and Pattern Recognition, Transportation and Management Science and Operations Research, having authored 6 papers that have together received 397 indexed citations. Recurring topics across this work include Video Surveillance and Tracking Methods (5 papers), Human Pose and Action Recognition (4 papers) and Gait Recognition and Analysis (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (364 citations), Biomedical Engineering (159 citations) and Safety, Risk, Reliability and Quality (22 citations). Zimo Liu has collaborated with scholars based in China, Australia and United Kingdom. Frequent co-authors include Huchuan Lu, Jingya Wang, Shang Gao, Dong Wang, Dacheng Tao, Shaogang Gong, Xiang Ruan, Huchuan Lu, Ming–Hsuan Yang and Qingmin Liao. Their work appears in journals such as IEEE Transactions on Neural Networks and Learning Systems, Queen Mary Research Online (Queen Mary University of London) and Proceedings of the AAAI Conference on Artificial Intelligence.
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.