Shenqi Lai
- Computer Vision and Pattern Recognition top 0.5%
- Biomedical Engineering top 10%
- Artificial Intelligence top 5%
- Media Technology top 5%
- Aerospace Engineering top 10%
- Topics
- Video Surveillance and Tracking Methods (12 papers)Face recognition and analysis (10 papers)Advanced Neural Network Applications (10 papers)
- Journals
- IEEE Transactions on Image ProcessingPattern RecognitionInternational Journal of Computer Vision
- Partner nations
- ChinaUnited StatesHong Kong
In The Last Decade
Shenqi Lai
26 papers receiving 1.8k citations
Hit Papers
Peers
Comparison fields: 5 of 102
- Computer Vision and Pattern Recognition 1.6k
- Biomedical Engineering 342
- Artificial Intelligence 333
- Media Technology 130
- Aerospace Engineering 114
Countries citing papers authored by Shenqi Lai
This map shows the geographic impact of Shenqi Lai'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 Shenqi Lai with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shenqi Lai more than expected).
Fields of papers citing papers by Shenqi Lai
This network shows the impact of papers produced by Shenqi Lai. 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 Shenqi Lai. The network helps show where Shenqi Lai may publish in the future.
Co-authorship network of co-authors of Shenqi Lai
This figure shows the co-authorship network connecting the top 25 collaborators of Shenqi Lai. A scholar is included among the top collaborators of Shenqi Lai 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 Shenqi Lai. Shenqi Lai 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 | 4 | |
| 3 | 14 | |
| 4 | 7 | |
| 5 | 3 | |
| 6 | 10 | |
| 7 | 9 | |
| 8 | 2 | |
| 9 | 6 | |
| 10 | 37 | |
| 11 | Rethinking BiSeNet For Real-time Semantic Segmentationbreakdown → | 494 |
| 12 | 61 | |
| 13 | 36 | |
| 14 | 55 | |
| 15 | 0 | |
| 16 | 11 | |
| 17 | Enhanced Normalized Mean Error loss for Robust Facial Landmark detection. | 3 |
| 18 | Bag of Tricks and a Strong Baseline for Deep Person Re-Identificationbreakdown → | 864 |
| 19 | 3 | |
| 20 | 2 |
About Shenqi Lai
Shenqi Lai is a scholar working on Computer Vision and Pattern Recognition, Signal Processing and Artificial Intelligence, having authored 27 papers that have together received 1.9k indexed citations. Recurring topics across this work include Video Surveillance and Tracking Methods (12 papers), Face recognition and analysis (10 papers) and Advanced Neural Network Applications (10 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (1.6k citations), Media Technology (130 citations) and Artificial Intelligence (333 citations). Shenqi Lai has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include Hao Luo, Xingyu Liao, Wei Jiang, Zhenhua Chai, Xiaolin Wei, Xueming Qian, Xiaoming Wei, Mingyuan Fan, Junshi Huang and Yan Yan. Their work appears in journals such as IEEE Transactions on Image Processing, Pattern Recognition and International Journal of Computer Vision.
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.