Qingsong Lv

1.2k total citations · 1 hit paper
14 papers, 572 citations indexed

About

Qingsong Lv is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Control and Systems Engineering. According to data from OpenAlex, Qingsong Lv has authored 14 papers receiving a total of 572 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Artificial Intelligence, 4 papers in Computer Vision and Pattern Recognition and 2 papers in Control and Systems Engineering. Recurrent topics in Qingsong Lv's work include Topic Modeling (4 papers), Multimodal Machine Learning Applications (3 papers) and Advanced Graph Neural Networks (2 papers). Qingsong Lv is often cited by papers focused on Topic Modeling (4 papers), Multimodal Machine Learning Applications (3 papers) and Advanced Graph Neural Networks (2 papers). Qingsong Lv collaborates with scholars based in China, United States and Iran. Qingsong Lv's co-authors include Zhichun Wang, Xiaohan Lan, Yu Zhang, Jie Tang, Yuxiao Dong, Qiang Liu, Yuxiang Chen, Ming Ding, Wenzheng Feng and Chang Zhou and has published in prestigious journals such as Chemical Communications, IEEE Transactions on Circuits and Systems for Video Technology and IEEE Transactions on Instrumentation and Measurement.

In The Last Decade

Qingsong Lv

13 papers receiving 558 citations

Hit Papers

Cross-lingual Knowledge G... 2018 2026 2020 2023 2018 100 200 300

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Qingsong Lv China 6 462 187 118 73 55 14 572
Hongyu Ren United States 11 407 0.9× 41 0.2× 165 1.4× 51 0.7× 21 0.4× 21 543
Linfeng Song China 16 959 2.1× 57 0.3× 191 1.6× 110 1.5× 15 0.3× 64 1.0k
Javad Azimi United States 9 225 0.5× 47 0.3× 117 1.0× 57 0.8× 30 0.5× 19 353
Shuangyin Li China 13 290 0.6× 27 0.1× 74 0.6× 89 1.2× 28 0.5× 37 426
Tian Gao United States 12 264 0.6× 74 0.4× 61 0.5× 52 0.7× 9 0.2× 37 351
Miao Fan China 14 408 0.9× 74 0.4× 188 1.6× 132 1.8× 24 0.4× 35 598
Wei-Wei Tu China 12 255 0.6× 26 0.1× 113 1.0× 71 1.0× 14 0.3× 34 383
Cheng Ji China 8 254 0.5× 21 0.1× 53 0.4× 49 0.7× 39 0.7× 18 370
Yizhen Zheng Australia 11 217 0.5× 22 0.1× 44 0.4× 90 1.2× 36 0.7× 20 294

Countries citing papers authored by Qingsong Lv

Since Specialization
Citations

This map shows the geographic impact of Qingsong Lv'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 Qingsong Lv with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Qingsong Lv more than expected).

Fields of papers citing papers by Qingsong Lv

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Qingsong Lv. 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 Qingsong Lv. The network helps show where Qingsong Lv may publish in the future.

Co-authorship network of co-authors of Qingsong Lv

This figure shows the co-authorship network connecting the top 25 collaborators of Qingsong Lv. A scholar is included among the top collaborators of Qingsong Lv 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 Qingsong Lv. Qingsong Lv is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

14 of 14 papers shown
1.
Liu, Junhui, et al.. (2025). New strategy for dimensional control of manganese-based metal halides: feed composition control. Chemical Communications. 61(92). 18184–18187.
2.
Huang, Wanwei, et al.. (2025). Faulty Links’ Fast Recovery Method Based on Deep Reinforcement Learning. Algorithms. 18(5). 241–241. 1 indexed citations
3.
Lv, Qingsong, et al.. (2024). RWS: Refined Weak Slice for Semantic Segmentation Enhancement. IEEE Transactions on Circuits and Systems for Video Technology. 34(7). 5704–5715. 2 indexed citations
4.
Hong, Wenyi, Weihan Wang, Qingsong Lv, et al.. (2024). CogAgent: A Visual Language Model for GUI Agents. 14281–14290. 43 indexed citations
5.
Chen, Keqin, Ming Ding, Yuxiao Dong, et al.. (2024). CogVLM: Visual Expert for Pretrained Language Models. 121475–121499. 2 indexed citations
6.
Lv, Qingsong, et al.. (2022). Small-Scale Robust Digital Recognition of Meters Under Unstable and Complex Conditions. IEEE Transactions on Instrumentation and Measurement. 71. 1–13. 9 indexed citations
7.
Yang, Zhuoyi, Ming Ding, Yanhui Guo, Qingsong Lv, & Jie Tang. (2022). Parameter-Efficient Tuning Makes a Good Classification Head. 7576–7586. 2 indexed citations
8.
Rao, Yunbo, Qingsong Lv, Shaoning Zeng, et al.. (2022). COVID-19 CT ground-glass opacity segmentation based on attention mechanism threshold. Biomedical Signal Processing and Control. 81. 104486–104486. 11 indexed citations
9.
Lv, Qingsong, Ming Ding, Qiang Liu, et al.. (2021). Are we really making much progress?. 1150–1160. 135 indexed citations
10.
Wang, Zhichun, Qingsong Lv, Xiaohan Lan, & Yu Zhang. (2018). Cross-lingual Knowledge Graph Alignment via Graph Convolutional Networks. 349–357. 345 indexed citations breakdown →
12.
Zhang, Lu, et al.. (2017). Single channel brain-computer interface control system based on TGAM module. 128. 1–5. 3 indexed citations
13.
Zhou, Ning, et al.. (2015). TPL-PCRUN Statement of methods. Vehicle System Dynamics. 53(3). 2 indexed citations
14.
Zhou, Ning, et al.. (2014). <TPL-PCRUN> Statement of methods. Vehicle System Dynamics. 53(3). 380–391. 14 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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