Shu Liu
- Computer Vision and Pattern Recognition top 2%
- Artificial Intelligence top 5%
- Electrical and Electronic Engineering
- Aerospace Engineering
- Biomedical Engineering
- Topics
- Face recognition and analysis (4 papers)Face and Expression Recognition (4 papers)Video Surveillance and Tracking Methods (3 papers)
- Journals
- SHILAP Revista de lepidopterologíaScientific ReportsChemosphere
- Partner nations
- ChinaUnited StatesHong Kong
In The Last Decade
Shu Liu
43 papers receiving 822 citations
Hit Papers
Peers
Comparison fields: 5 of 121
- Computer Vision and Pattern Recognition 391
- Artificial Intelligence 347
- Electrical and Electronic Engineering 66
- Aerospace Engineering 59
- Biomedical Engineering 56
Countries citing papers authored by Shu Liu
This map shows the geographic impact of Shu 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 Shu Liu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shu Liu more than expected).
Fields of papers citing papers by Shu Liu
This network shows the impact of papers produced by Shu 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 Shu Liu. The network helps show where Shu Liu may publish in the future.
Co-authorship network of co-authors of Shu Liu
This figure shows the co-authorship network connecting the top 25 collaborators of Shu Liu. A scholar is included among the top collaborators of Shu 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 Shu Liu. Shu 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 | 4 | |
| 2 | 1 | |
| 3 | 3 | |
| 4 | LISA: Reasoning Segmentation via Large Language Modelbreakdown → | 96 |
| 5 | 3 | |
| 6 | 1 | |
| 7 | 6 | |
| 8 | 8 | |
| 9 | 16 | |
| 10 | 64 | |
| 11 | Distilling Knowledge via Knowledge Reviewbreakdown → | 285 |
| 12 | 49 | |
| 13 | Towards the Healing Car: Investigating the Potential of Psychotherapeutic In-vehicle Interventions | 1 |
| 14 | 5 | |
| 15 | Sequential Context Encoding for Duplicate Removal | 6 |
| 16 | 5 | |
| 17 | 1 | |
| 18 | Existence of positive solutions for boundary-value problems with integral boundary conditions and sign changing nonlinearities | 6 |
| 19 | Search Strategy and Achieve of the Topic Search Engine Spider | 0 |
| 20 | An experiment study on one and the same problem's decision projects founded by multigroup | 0 |
About Shu Liu
Shu Liu is a scholar working on Computer Vision and Pattern Recognition, Experimental and Cognitive Psychology and Artificial Intelligence, having authored 48 papers that have together received 838 indexed citations. Recurring topics across this work include Face recognition and analysis (4 papers), Face and Expression Recognition (4 papers) and Video Surveillance and Tracking Methods (3 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (391 citations), Artificial Intelligence (347 citations) and Media Technology (48 citations). Shu Liu has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include Jiaya Jia, Pengguang Chen, Hengshuang Zhao, Yangyu Fan, Xin Lai, Zhuotao Tian, Yanwei Li, Yukang Chen, Shenhua Song and Michelle Shu. Their work appears in journals such as SHILAP Revista de lepidopterología, Scientific Reports and Chemosphere.
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.