Yaochen Li

759 citations
66 papers · 513 · h-index 12

Impact in

Papers in

Yaochen Li

62 papers receiving 499 citations

Peers

Yaochen Li
Comparison fields: 5 of 72
  • Computer Vision and Pattern Recognition 300
  • Computational Mathematics 7
  • Media Technology 66
  • Automotive Engineering 63
  • Computer Graphics and Computer-Aided Design 16
Replace Yunyang Xiong with:
Yunyang Xiong United States
Yongsheng Dong China
Haiyun Guo China
Ankur Datta United States
Siva Karthik Mustikovela Germany
Peng Chang China
Zehui Chen China
Yiming Cui United States
Hassan Abu Alhaija Germany
Ziyun Cai China
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Citations per year

Countries citing papers authored by Yaochen Li

Since Specialization
Citations

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

Fields of papers citing papers by Yaochen Li

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Yaochen Li, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Yaochen Li Line = papers co-authored together Yaochen Li links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 66 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201998
2 202040
3 202338
4 202329
5 202123
6 201621
7 201820
8 201515
9 201713
10 202012
11 201911
12 201511
13 201110
14 202010
15 201910
16 201410
17 202210
18 20218
19 20187
20 20207

About Yaochen Li

Yaochen Li is a scholar working on Computer Vision and Pattern Recognition, Automotive Engineering, Aerospace Engineering, Computational Mechanics and Environmental Engineering, having authored 66 papers that have together received 513 indexed citations. Recurring topics across this work include Video Surveillance and Tracking Methods (12 papers), Advanced Neural Network Applications (11 papers), Autonomous Vehicle Technology and Safety (10 papers), Advanced Vision and Imaging (9 papers), Robotics and Sensor-Based Localization (9 papers), Remote Sensing and LiDAR Applications (8 papers), Advanced Image and Video Retrieval Techniques (8 papers) and 3D Shape Modeling and Analysis (6 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (300 citations), Computational Mathematics (7 citations), Media Technology (66 citations), Automotive Engineering (63 citations) and Computer Graphics and Computer-Aided Design (16 citations). Yaochen Li has collaborated with scholars based in China, United States and Japan. Frequent co-authors include Jihua Zhu, Shanmin Pang, Zhongyu Li, Qinghai Zheng, Jun Wang, Yuehu Liu, Yuehu Liu, Nanning Zheng, Ruihao Wang and Jian G. Qin. Their work appears in journals such as Neurocomputing, IEEE Transactions on Intelligent Transportation Systems, Multimedia Tools and Applications, Electronics Letters and Sensors.

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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