Guanglu Song

2.3k total citations · 4 hit papers
22 papers, 1.1k citations indexed

About

Guanglu Song is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Biomedical Engineering. According to data from OpenAlex, Guanglu Song has authored 22 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Computer Vision and Pattern Recognition, 9 papers in Artificial Intelligence and 2 papers in Biomedical Engineering. Recurrent topics in Guanglu Song's work include Advanced Neural Network Applications (8 papers), Video Surveillance and Tracking Methods (6 papers) and Advanced Image and Video Retrieval Techniques (6 papers). Guanglu Song is often cited by papers focused on Advanced Neural Network Applications (8 papers), Video Surveillance and Tracking Methods (6 papers) and Advanced Image and Video Retrieval Techniques (6 papers). Guanglu Song collaborates with scholars based in China, Hong Kong and Canada. Guanglu Song's co-authors include Yu Liu, Xiaogang Wang, Zhuofan Zong, Hongsheng Li, Yu Qiao, Yali Wang, Kunchang Li, Junhao Zhang, Peng Gao and Biao Leng and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Access and Neurocomputing.

In The Last Decade

Guanglu Song

20 papers receiving 1.1k citations

Hit Papers

Revisiting the Sibling He... 2020 2026 2022 2024 2020 2023 2023 2024 100 200 300
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Revisiting the Sibling Head in Object Detector
    2020 312 citations Guanglu Song, Yu Liu et al. profile →
  2. UniFormer: Unifying Convolution and Self-Attention for Visual Recognition
    2023 295 citations Kunchang Li, Yali Wang et al. IEEE Transactions on Pattern Analysis and Machine Intelligence profile →
  3. DETRs with Collaborative Hybrid Assignments Training
    2023 229 citations Zhuofan Zong, Guanglu Song et al. profile →
  4. LMDrive: Closed-Loop End-to-End Driving with Large Language Models
    2024 50 citations Hao Shao, Yuxuan Hu et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Guanglu Song China 9 775 246 142 121 118 22 1.1k
Siyuan Qiao United States 13 1.0k 1.3× 375 1.5× 185 1.3× 70 0.6× 167 1.4× 25 1.4k
Lingxiao Yang China 9 854 1.1× 178 0.7× 104 0.7× 60 0.5× 160 1.4× 23 1.2k
Qijie Zhao China 13 988 1.3× 259 1.1× 159 1.1× 71 0.6× 179 1.5× 56 1.3k
Zhanghan Ke Hong Kong 6 542 0.7× 206 0.8× 86 0.6× 56 0.5× 140 1.2× 10 858
Diganta Misra India 4 552 0.7× 187 0.8× 99 0.7× 72 0.6× 128 1.1× 8 958
Bharat Singh India 10 951 1.2× 370 1.5× 131 0.9× 58 0.5× 102 0.9× 21 1.2k
Tengteng Huang China 7 930 1.2× 188 0.8× 203 1.4× 135 1.1× 81 0.7× 7 1.1k
Shilong Liu China 11 728 0.9× 280 1.1× 177 1.2× 42 0.3× 115 1.0× 22 1.1k

Countries citing papers authored by Guanglu Song

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

20 of 20 papers shown
1.
Shao, Hao, Yuxuan Hu, Letian Wang, et al.. (2024). LMDrive: Closed-Loop End-to-End Driving with Large Language Models. 15120–15130. 50 indexed citations breakdown →
2.
Li, Hongsheng, et al.. (2024). CoMat: Aligning Text-to-Image Diffusion Model with Image-to-Text Concept Matching. 76177–76209. 2 indexed citations
3.
Shen, Dazhong, et al.. (2024). Rethinking the Spatial Inconsistency in Classifier-Free Diffusion Guidance. 9370–9379. 1 indexed citations
4.
Huang, Zhaoyang, Hongsheng Li, Yü Liu, et al.. (2024). Phased Consistency Models. 83951–84009.
5.
6.
Li, Hongsheng, Yu Liu, Hao Shao, et al.. (2024). MoVA: Adapting Mixture of Vision Experts to Multimodal Context. 103305–103333. 1 indexed citations
7.
Zong, Zhuofan, Guanglu Song, & Yu Liu. (2023). DETRs with Collaborative Hybrid Assignments Training. 6725–6735. 229 indexed citations breakdown →
8.
Huang, Linjiang, Kaixin Lu, Guanglu Song, et al.. (2023). Teach-DETR: Better Training DETR With Teachers. IEEE Transactions on Pattern Analysis and Machine Intelligence. 45(12). 15759–15771. 8 indexed citations
9.
10.
Lao, Shanshan, et al.. (2023). Masked Autoencoders Are Stronger Knowledge Distillers. 6361–6370. 5 indexed citations
11.
Zong, Zhuofan, et al.. (2023). Temporal Enhanced Training of Multi-view 3D Object Detector via Historical Object Prediction. 3758–3767. 24 indexed citations
12.
Li, Kunchang, Yali Wang, Junhao Zhang, et al.. (2023). UniFormer: Unifying Convolution and Self-Attention for Visual Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence. 45(10). 12581–12600. 295 indexed citations breakdown →
13.
Lao, Shanshan, et al.. (2023). UniKD: Universal Knowledge Distillation for Mimicking Homogeneous or Heterogeneous Object Detectors. 6339–6349. 4 indexed citations
14.
Song, Guanglu, et al.. (2021). Rectifying the Data Bias in Knowledge Distillation. 1477–1486. 8 indexed citations
15.
Song, Guanglu, et al.. (2021). Switchable K-class Hyperplanes for Noise-Robust Representation Learning. 2021 IEEE/CVF International Conference on Computer Vision (ICCV). 2999–3008. 3 indexed citations
16.
Song, Guanglu, Yu Liu, & Xiaogang Wang. (2020). Revisiting the Sibling Head in Object Detector. 11560–11569. 312 indexed citations breakdown →
17.
Leng, Biao, et al.. (2019). Weighted triple-sequence loss for video-based person re-identification. Neurocomputing. 381. 314–321. 11 indexed citations
18.
Leng, Biao, et al.. (2019). Spatial-Transformed Regional Quality Estimation Network for Large-Variance Person Re-Identification. IEEE Access. 7. 118101–118111. 1 indexed citations
19.
Leng, Biao, et al.. (2019). Scale Pyramid Attention for Single Shot MultiBox Detector. IEEE Access. 7. 138816–138824. 1 indexed citations
20.
Song, Guanglu, et al.. (2018). Region-Based Quality Estimation Network for Large-Scale Person Re-Identification. Proceedings of the AAAI Conference on Artificial Intelligence. 32(1). 114 indexed citations

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.

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