Linlin Liu
Impact in
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- Generative Adversarial Networks and Image Synthesis
- Advanced Neural Network Applications
- Video Surveillance and Tracking Methods
- Face recognition and analysis
- Advanced Image Processing Techniques
Papers in
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- Generative Adversarial Networks and Image Synthesis 13
- Face recognition and analysis 6
- Advanced Image Processing Techniques 3
- Human Pose and Action Recognition 3
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- 3D Shape Modeling and Analysis 7
- Co-authors
- Haijun Zhang (14 shared papers)Yuzhu Ji (3 shared papers)Xiaofei Xu (5 shared papers)Zhao Zhang (4 shared papers)Q. M. Jonathan Wu (1 shared paper)Shuicheng Yan (3 shared papers)Dongliang Zhou (5 shared papers)Jun Wang (1 shared paper)
In The Last Decade
Linlin Liu
22 papers receiving 586 citations
Peers
Comparison fields: 5 of 93
- Computer Graphics and Computer-Aided Design 66
- Computer Vision and Pattern Recognition 329
- Museology 25
- Computational Mechanics 86
- Media Technology 30
Countries citing papers authored by Linlin Liu
This map shows the geographic impact of Linlin 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 Linlin Liu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Linlin Liu more than expected).
Fields of papers citing papers by Linlin Liu
This network shows the impact of papers produced by Linlin 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 Linlin Liu. The network helps show where Linlin Liu may publish in the future.
Co-authors
The 25 scholars most cited alongside Linlin Liu, 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 22 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2019 | 83 | |
| 2 | 2018 | 78 | |
| 3 | 2018 | 66 | |
| 4 | 2020 | 58 | |
| 5 | 2019 | 54 | |
| 6 | 2022 | 52 | |
| 7 | 2018 | 39 | |
| 8 | 2018 | 28 | |
| 9 | 2017 | 26 | |
| 10 | 2022 | 19 | |
| 11 | 2019 | 18 | |
| 12 | 2023 | 17 | |
| 13 | 2018 | 11 | |
| 14 | 2022 | 11 | |
| 15 | 2021 | 9 | |
| 16 | 2019 | 9 | |
| 17 | 2021 | 7 | |
| 18 | 2025 | 5 | |
| 19 | 2023 | 5 | |
| 20 | 2018 | 5 |
About Linlin Liu
Linlin Liu is a scholar working on Computer Vision and Pattern Recognition, Computational Mechanics, Cognitive Neuroscience, Computer Graphics and Computer-Aided Design and Computer Networks and Communications, having authored 22 papers that have together received 607 indexed citations. Recurring topics across this work include Generative Adversarial Networks and Image Synthesis (13 papers), 3D Shape Modeling and Analysis (7 papers), Face recognition and analysis (6 papers), Advanced Image Processing Techniques (3 papers), Human Pose and Action Recognition (3 papers), Computer Graphics and Visualization Techniques (3 papers), Neural Networks Stability and Synchronization (2 papers) and Aesthetic Perception and Analysis (2 papers). The work is most often cited by research in Computer Graphics and Computer-Aided Design (66 citations), Computer Vision and Pattern Recognition (329 citations), Museology (25 citations), Computational Mechanics (86 citations) and Media Technology (30 citations). Linlin Liu has collaborated with scholars based in China, Singapore and Hong Kong. Frequent co-authors include Haijun Zhang, Yuzhu Ji, Xiaofei Xu, Zhao Zhang, Q. M. Jonathan Wu, Shuicheng Yan, Dongliang Zhou, Jun Wang, Zhenyuan Guo and Wenyin Liu. Their work appears in journals such as Neural Computing and Applications, Neurocomputing, IEEE Transactions on Neural Networks and Learning Systems, Scientific Reports and Chemical Communications.
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.