Lianli Gao
- Computer Vision and Pattern Recognition top 0.1%
- Artificial Intelligence top 0.5%
- Biomedical Engineering top 10%
- Signal Processing top 2%
- Media Technology top 2%
- Co-authors
- Jingkuan SongHeng Tao ShenXing XuXuanhan WangXiangpeng LiHanwang ZhangZhao GuoPengpeng Zeng
- Topics
- Multimodal Machine Learning Applications (90 papers)Advanced Image and Video Retrieval Techniques (78 papers)Human Pose and Action Recognition (48 papers)
- Journals
- PLoS ONEIEEE Transactions on Pattern Analysis and Machine IntelligenceIEEE Transactions on Image Processing
- Partner nations
- ChinaAustraliaUnited States
In The Last Decade
Lianli Gao
180 papers receiving 5.6k citations
Hit Papers
Peers
Comparison fields: 5 of 154
- Computer Vision and Pattern Recognition 4.4k
- Artificial Intelligence 2.3k
- Biomedical Engineering 268
- Signal Processing 265
- Media Technology 242
Countries citing papers authored by Lianli Gao
This map shows the geographic impact of Lianli Gao'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 Lianli Gao with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Lianli Gao more than expected).
Fields of papers citing papers by Lianli Gao
This network shows the impact of papers produced by Lianli Gao. 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 Lianli Gao. The network helps show where Lianli Gao may publish in the future.
Co-authorship network of co-authors of Lianli Gao
This figure shows the co-authorship network connecting the top 25 collaborators of Lianli Gao. A scholar is included among the top collaborators of Lianli Gao 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 Lianli Gao. Lianli Gao 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 | 0 | |
| 3 | 1 | |
| 4 | 0 | |
| 5 | 3 | |
| 6 | 4 | |
| 7 | 14 | |
| 8 | 2 | |
| 9 | 5 | |
| 10 | 4 | |
| 11 | 3 | |
| 12 | 15 | |
| 13 | 12 | |
| 14 | 4 | |
| 15 | 12 | |
| 16 | 20 | |
| 17 | 35 | |
| 18 | 57 | |
| 19 | 206 | |
| 20 | Semantic-based detection of segment outliers and unusual events for wireless sensor networks | 6 |
About Lianli Gao
Lianli Gao is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Signal Processing, having authored 185 papers that have together received 5.7k indexed citations. Recurring topics across this work include Multimodal Machine Learning Applications (90 papers), Advanced Image and Video Retrieval Techniques (78 papers) and Human Pose and Action Recognition (48 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (4.4k citations), Artificial Intelligence (2.3k citations) and Media Technology (242 citations). Lianli Gao has collaborated with scholars based in China, Australia and United States. Frequent co-authors include Jingkuan Song, Heng Tao Shen, Xing Xu, Xuanhan Wang, Xiangpeng Li, Hanwang Zhang, Zhao Guo, Pengpeng Zeng, Xianglong Liu and Nicu Sebe. Their work appears in journals such as PLoS ONE, IEEE Transactions on Pattern Analysis and Machine Intelligence 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.