Kai Ye

607 citations
22 papers · 343 · h-index 8

Impact in

Papers in

Kai Ye

18 papers receiving 332 citations

Peers

Kai Ye
Comparison fields: 5 of 79
  • Computer Graphics and Computer-Aided Design 26
  • Computer Vision and Pattern Recognition 152
  • Software 17
  • Toxicology 12
  • Artificial Intelligence 115
Replace R. H. Perrott with:
R. H. Perrott United Kingdom
Qingqiang Wu China
Hanjun Kim South Korea
Jamison D. Collins United States
Antonio J. Peña Spain
Richard Borie United States
Michael D. Moffitt United States
Beat Gfeller Switzerland
Laura A. Sanchis United States
Markus Chimani Germany
Kai Ye relative to R. H. Perrott United Kingdom R. H. Perrott's profile →
Citations per field
00.5×10×20×30×40×47×
R. H. Perrott · 1×
Citations per year

Countries citing papers authored by Kai Ye

Since Specialization
Citations

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

Fields of papers citing papers by Kai Ye

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 22 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2022113
2 201850
3 202036
4 200836
5 200436
6 201918
7 202214
8 20247
9 20217
10
Bibliometric analysis of author collaboration in engineering management research
20155
11 20224
12 20234
13 20224
14 20183
15 20252
16 20172
17
Data Transformation Between XSD-based XML Document And Relational Database
20071
18 20171
19 20240
20 20230

About Kai Ye

Kai Ye is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Computer Networks and Communications, Signal Processing and Computer Science Applications, having authored 22 papers that have together received 343 indexed citations. Recurring topics across this work include 3D Surveying and Cultural Heritage (2 papers), Multimodal Machine Learning Applications (2 papers), Domain Adaptation and Few-Shot Learning (2 papers), Music and Audio Processing (2 papers), Advanced Image and Video Retrieval Techniques (2 papers), Speech Recognition and Synthesis (2 papers), Mobile Crowdsensing and Crowdsourcing (2 papers) and Personal Information Management and User Behavior (2 papers). The work is most often cited by research in Computer Graphics and Computer-Aided Design (26 citations), Computer Vision and Pattern Recognition (152 citations), Software (17 citations), Toxicology (12 citations) and Artificial Intelligence (115 citations). Kai Ye has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include Linlin Shen, Xianxu Hou, Jinheng Xie, Shunzhi Yang, Zheng Gong, Kun Zhou, Li Li, Jiaoying Shi, Changjiang Zhou and Zhaolin Sun. Their work appears in journals such as Computers & Graphics, IEEE Transactions on Multimedia, Journal of Ambient Intelligence and Humanized Computing, Journal of the Association for Information Systems and Proceedings of the ACM on Human-Computer Interaction.

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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