Guanglu Song
- Computer Vision and Pattern Recognition top 1%
- Artificial Intelligence top 5%
- Aerospace Engineering top 10%
- Biomedical Engineering
- Media Technology top 5%
- Topics
- Advanced Neural Network Applications (8 papers)Video Surveillance and Tracking Methods (6 papers)Advanced Image and Video Retrieval Techniques (6 papers)
In The Last Decade
Guanglu Song
20 papers receiving 1.1k citations
Hit Papers
Peers
Comparison fields: 5 of 105
- Computer Vision and Pattern Recognition 775
- Artificial Intelligence 246
- Aerospace Engineering 142
- Biomedical Engineering 121
- Media Technology 118
Countries citing papers authored by Guanglu Song
This map shows the geographic impact of Guanglu Song'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 Guanglu Song with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Guanglu Song more than expected).
Fields of papers citing papers by Guanglu Song
This network shows the impact of papers produced by Guanglu Song. 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 Guanglu Song. The network helps show where Guanglu Song may publish in the future.
Co-authorship network of co-authors of Guanglu Song
This figure shows the co-authorship network connecting the top 25 collaborators of Guanglu Song. A scholar is included among the top collaborators of Guanglu Song 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 Guanglu Song. Guanglu Song is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | LMDrive: Closed-Loop End-to-End Driving with Large Language Modelsbreakdown → | 50 |
| 2 | 1 | |
| 3 | 2 | |
| 4 | 0 | |
| 5 | 1 | |
| 6 | DETRs with Collaborative Hybrid Assignments Trainingbreakdown → | 229 |
| 7 | 8 | |
| 8 | UniFormer: Unifying Convolution and Self-Attention for Visual Recognitionbreakdown → | 295 |
| 9 | 4 | |
| 10 | 10 | |
| 11 | 5 | |
| 12 | 24 | |
| 13 | 3 | |
| 14 | 8 | |
| 15 | Revisiting the Sibling Head in Object Detectorbreakdown → | 312 |
| 16 | 6 | |
| 17 | 11 | |
| 18 | 1 | |
| 19 | 1 | |
| 20 | 114 |
About Guanglu Song
Guanglu Song is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Software, having authored 22 papers that have together received 1.1k indexed citations. Recurring topics across this work include Advanced Neural Network Applications (8 papers), Video Surveillance and Tracking Methods (6 papers) and Advanced Image and Video Retrieval Techniques (6 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (775 citations), Media Technology (118 citations) and Industrial and Manufacturing Engineering (85 citations). Guanglu Song has collaborated with scholars based in China, Hong Kong and Canada. Frequent co-authors include Yu Liu, Xiaogang Wang, Zhuofan Zong, Hongsheng Li, Yu Qiao, Junhao Zhang, Yali Wang, Kunchang Li, Peng Gao and Biao Leng. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Access and Neurocomputing.
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.