Kai Lv

614 citations
42 papers · 377 · h-index 11

Impact in

Papers in

Kai Lv

37 papers receiving 368 citations

Peers

Kai Lv
Comparison fields: 5 of 92
  • Computer Vision and Pattern Recognition 126
  • Speech and Hearing 32
  • Human-Computer Interaction 22
  • Automotive Engineering 31
  • Catalysis 16
Replace Xiaogang Cheng with:
Xiaogang Cheng China
Haoran Wu China
John Chiverton United Kingdom
Hongying Yu China
Ruyi Jiang China
Sakthivel Sankaran India
Qingyi Liu China
Yaguang Li China
Kwang‐Ju Kim South Korea
Kai Lv relative to Xiaogang Cheng China Xiaogang Cheng's profile →
Citations per field
00.5×2.8×
Xiaogang Cheng · 1×
Citations per year

Countries citing papers authored by Kai Lv

Since Specialization
Citations

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

Fields of papers citing papers by Kai Lv

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Kai Lv, 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 Kai Lv Line = papers co-authored together Kai Lv links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 42 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201851
2 201940
3 202038
4 202030
5 202026
6 202022
7 202319
8 202415
9 202215
10 201214
11 202411
12
Vehicle Re-Identification with Location and Time Stamps
201910
13 20209
14 20249
15 20197
16 20236
17 20196
18 20245
19 20165
20 20175

About Kai Lv

Kai Lv is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Electrical and Electronic Engineering, Control and Systems Engineering and Aerospace Engineering, having authored 42 papers that have together received 377 indexed citations. Recurring topics across this work include Video Surveillance and Tracking Methods (11 papers), Reinforcement Learning in Robotics (4 papers), Smart Grid Energy Management (4 papers), Microgrid Control and Optimization (3 papers), Advanced Neural Network Applications (3 papers), Infrared Target Detection Methodologies (3 papers), Generative Adversarial Networks and Image Synthesis (3 papers) and Advanced Image and Video Retrieval Techniques (3 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (126 citations), Speech and Hearing (32 citations), Human-Computer Interaction (22 citations), Automotive Engineering (31 citations) and Catalysis (16 citations). Kai Lv has collaborated with scholars based in China, Australia and Macao. Frequent co-authors include Hao Sheng, Zhang Xiong, Liang Zheng, Youfang Lin, Xiaobo Wang, Guoyun Lu, Zengli Zhao, Wei Li, Xiaofeng Shi and Guoqiang Wei. Their work appears in journals such as IEEE Access, IEEE Transactions on Multimedia, ACM Transactions on Multimedia Computing Communications and Applications, IEEE Transactions on Intelligent Transportation Systems and International Orthopaedics.

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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