Jinlong Li

668 total citations · 1 hit paper
9 papers, 400 citations indexed

About

Jinlong Li is a scholar working on Computer Vision and Pattern Recognition, Automotive Engineering and Artificial Intelligence. According to data from OpenAlex, Jinlong Li has authored 9 papers receiving a total of 400 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Computer Vision and Pattern Recognition, 4 papers in Automotive Engineering and 2 papers in Artificial Intelligence. Recurrent topics in Jinlong Li's work include Advanced Neural Network Applications (5 papers), Autonomous Vehicle Technology and Safety (4 papers) and Video Surveillance and Tracking Methods (3 papers). Jinlong Li is often cited by papers focused on Advanced Neural Network Applications (5 papers), Autonomous Vehicle Technology and Safety (4 papers) and Video Surveillance and Tracking Methods (3 papers). Jinlong Li collaborates with scholars based in United States, China and Singapore. Jinlong Li's co-authors include Runsheng Xu, Jiaqi Ma, Xu Han, Hao Xiang, Xin Xia, Hongkai Yu, Jin Ma, Zicheng Chi, Tianyun Zhang and Zhigang Xu and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Transactions on Intelligent Vehicles and Array.

In The Last Decade

Jinlong Li

8 papers receiving 393 citations

Hit Papers

align trajectories

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.

OPV2V: An Open Benchmark Dataset and Fusion Pipeline for Perception with Vehicle-to-Vehicle Communication

2022 261 citations Runsheng Xu, Hao Xiang et al. 2022 International Conference on Robotics and Automation (ICRA) profile →

Peers

Jinlong Li

CVPR

AE

EEE

AI

AE

Jamil Fayyad Canada

Pablo Marín Spain

Peter Ondrúška United Kingdom

Yuki Kitsukawa Japan

Jelena Kocić Serbia

Jean-Philippe Lauffenburger France

Kyounghwan An South Korea

Maximilian Geisslinger Germany

Zhongyang Xiao China

Jamil Fayyad Canada

Jinlong Li

145 ×0.7

CVPR

121 ×0.8

AE

70 ×0.9

EEE

77 ×1.1

AI

100 ×1.4

AE

Citations per year, relative to Jinlong Li Jinlong Li (= 1×) peers Jamil Fayyad

Countries citing papers authored by Jinlong Li

Since Specialization

Citations

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

Fields of papers citing papers by Jinlong Li

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jinlong Li

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

All Works

Sort: Min cites: Since: Top N: Style:

9 of 9 papers shown

1.

Li, Jinlong, et al.. (2025). FedDDPG: A reinforcement learning method for federated learning-based vehicle trajectory prediction. Array. 27. 100450–100450. 2 indexed citations

2.

Liu, Ping, et al.. (2025). Enhancing vehicle Re-identification by pair-flexible pose guided vehicle image synthesis. Green Energy and Intelligent Transportation. 4(5). 100269–100269. 1 indexed citations

3.

Li, Jinlong, et al.. (2025). V2X-DGW: Domain Generalization for Multi-Agent Perception Under Adverse Weather Conditions. 974–980.

4.

Li, Jinlong, et al.. (2024). Domain Adaptation Based Object Detection for Autonomous Driving in Foggy and Rainy Weather. IEEE Transactions on Intelligent Vehicles. 10(2). 900–911. 8 indexed citations

5.

Xu, Runsheng, et al.. (2024). V2X-DSI: A Density-Sensitive Infrastructure LiDAR Benchmark for Economic Vehicle-to-Everything Cooperative Perception. 490–495. 2 indexed citations

6.

Fu, Lan, Jinlong Li, Qing Guo, et al.. (2024). Defense against Adversarial Cloud Attack on Remote Sensing Salient Object Detection. 8330–8339. 10 indexed citations

7.

Li, Jinlong, et al.. (2023). Deep transfer learning for intelligent vehicle perception: A survey. SHILAP Revista de lepidopterología. 2(5). 100125–100125. 30 indexed citations

8.

Li, Jinlong, Runsheng Xu, Jin Ma, et al.. (2023). Learning for Vehicle-to-Vehicle Cooperative Perception Under Lossy Communication. IEEE Transactions on Intelligent Vehicles. 8(4). 2650–2660. 86 indexed citations

9.

Xu, Runsheng, Hao Xiang, Xin Xia, et al.. (2022). OPV2V: An Open Benchmark Dataset and Fusion Pipeline for Perception with Vehicle-to-Vehicle Communication. 2022 International Conference on Robotics and Automation (ICRA). 2583–2589. 261 indexed citations breakdown →

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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