Yuejiang Liu
- Computer Vision and Pattern Recognition top 2%
- Artificial Intelligence top 5%
- Automotive Engineering top 5%
- Ocean Engineering top 5%
- Control and Systems Engineering top 10%
- Topics
- Anomaly Detection Techniques and Applications (3 papers)Generative Adversarial Networks and Image Synthesis (2 papers)Time Series Analysis and Forecasting (2 papers)
- Journals
- 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)2021 IEEE/CVF International Conference on Computer Vision (ICCV)Infoscience (Ecole Polytechnique Fédérale de Lausanne)
- Partner nations
- SwitzerlandChinaFrance
In The Last Decade
Yuejiang Liu
9 papers receiving 571 citations
Hit Papers
Peers
Comparison fields: 5 of 58
- Computer Vision and Pattern Recognition 367
- Artificial Intelligence 288
- Automotive Engineering 219
- Ocean Engineering 147
- Control and Systems Engineering 82
Countries citing papers authored by Yuejiang Liu
This map shows the geographic impact of Yuejiang 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 Yuejiang Liu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yuejiang Liu more than expected).
Fields of papers citing papers by Yuejiang Liu
This network shows the impact of papers produced by Yuejiang 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 Yuejiang Liu. The network helps show where Yuejiang Liu may publish in the future.
Co-authorship network of co-authors of Yuejiang Liu
This figure shows the co-authorship network connecting the top 25 collaborators of Yuejiang Liu. A scholar is included among the top collaborators of Yuejiang 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 Yuejiang Liu. Yuejiang 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 | 0 | |
| 2 | 28 | |
| 3 | TTT++: When Does Self-Supervised Test-Time Training Fail or Thrive? | 60 |
| 4 | 17 | |
| 5 | 82 | |
| 6 | 10 | |
| 7 | Collaborative GAN Sampling | 1 |
| 8 | Crowd-Robot Interaction: Crowd-Aware Robot Navigation With Attention-Based Deep Reinforcement Learningbreakdown → | 367 |
| 9 | 21 | |
| 10 | 2 |
About Yuejiang Liu
Yuejiang Liu is a scholar working on Health Informatics, Computer Vision and Pattern Recognition and Applied Psychology, having authored 10 papers that have together received 588 indexed citations. Recurring topics across this work include Anomaly Detection Techniques and Applications (3 papers), Generative Adversarial Networks and Image Synthesis (2 papers) and Time Series Analysis and Forecasting (2 papers). The work is most often cited by research in Automotive Engineering (219 citations), Computer Vision and Pattern Recognition (367 citations) and Ocean Engineering (147 citations). Yuejiang Liu has collaborated with scholars based in Switzerland, China and France. Frequent co-authors include Alexandre Alahi, S. Kreiss, Changan Chen, Qi Yan, Parth Kothari, Taylor Mordan, An Xu and Colin N. Jones. Their work appears in journals such as 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 IEEE/CVF International Conference on Computer Vision (ICCV) and Infoscience (Ecole Polytechnique Fédérale de Lausanne).
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.