Chenliang Xu
- Computer Vision and Pattern Recognition top 0.5%
- Artificial Intelligence top 2%
- Signal Processing top 2%
- Media Technology top 2%
- Control and Systems Engineering top 10%
- Topics
- Multimodal Machine Learning Applications (21 papers)Human Pose and Action Recognition (19 papers)Speech and Audio Processing (14 papers)
- Journals
- SHILAP Revista de lepidopterologíaPLoS ONEIEEE Transactions on Image Processing
- Partner nations
- United StatesChinaHong Kong
In The Last Decade
Chenliang Xu
79 papers receiving 2.6k citations
Hit Papers
Peers
Comparison fields: 5 of 117
- Computer Vision and Pattern Recognition 2.2k
- Artificial Intelligence 580
- Signal Processing 410
- Media Technology 227
- Control and Systems Engineering 109
Countries citing papers authored by Chenliang Xu
This map shows the geographic impact of Chenliang Xu'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 Chenliang Xu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Chenliang Xu more than expected).
Fields of papers citing papers by Chenliang Xu
This network shows the impact of papers produced by Chenliang Xu. 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 Chenliang Xu. The network helps show where Chenliang Xu may publish in the future.
Co-authorship network of co-authors of Chenliang Xu
This figure shows the co-authorship network connecting the top 25 collaborators of Chenliang Xu. A scholar is included among the top collaborators of Chenliang Xu 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 Chenliang Xu. Chenliang Xu 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 | 0 | |
| 3 | Video Understanding With Large Language Models: A Surveybreakdown → | 16 |
| 4 | 4 | |
| 5 | 1 | |
| 6 | 10 | |
| 7 | 6 | |
| 8 | 116 | |
| 9 | 25 | |
| 10 | What comprises a good talking-head video generation? | 3 |
| 11 | 6 | |
| 12 | 2 | |
| 13 | Deep Audio Prior: Learning Sound Source Separation from a Single Audio Mixture | 2 |
| 14 | 19 | |
| 15 | Audio-Visual Interpretable and Controllable Video Captioning | 9 |
| 16 | 3 | |
| 17 | Sound to Visual: Hierarchical Cross-Modal Talking Face Generation | 4 |
| 18 | Audio-Visual Event Localization in the Wild | 4 |
| 19 | 75 | |
| 20 | ProcNets: Learning to Segment Procedures in Untrimmed and Unconstrained Videos. | 4 |
About Chenliang Xu
Chenliang Xu is a scholar working on Computer Vision and Pattern Recognition, Signal Processing and Artificial Intelligence, having authored 86 papers that have together received 2.7k indexed citations. Recurring topics across this work include Multimodal Machine Learning Applications (21 papers), Human Pose and Action Recognition (19 papers) and Speech and Audio Processing (14 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (2.2k citations), Signal Processing (410 citations) and Media Technology (227 citations). Chenliang Xu has collaborated with scholars based in United States, China and Hong Kong. Frequent co-authors include Jason J. Corso, Yapeng Tian, Luowei Zhou, Lele Chen, Yun Fu, Yulun Zhang, Zhiyao Duan, Ross K. Maddox, Jiebo Luo and Li Ding. Their work appears in journals such as SHILAP Revista de lepidopterología, PLoS ONE and IEEE Transactions on Image Processing.
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.