Mao Ye

9.9k citations
318 papers · 6.0k indexed · 3 hit papers · h-index 33
Topics
Video Surveillance and Tracking Methods (36 papers)Domain Adaptation and Few-Shot Learning (36 papers)Advanced Neural Network Applications (32 papers)

In The Last Decade

Mao Ye

277 papers receiving 5.6k citations

Hit Papers

Exploiting geographical influence for collaborative point...2005202620122019201120052007250500750

Peers

Mao Ye
Comparison fields: 5 of 159
  • Computer Vision and Pattern Recognition 2.1k
  • Computer Networks and Communications 1.6k
  • Artificial Intelligence 1.4k
  • Electrical and Electronic Engineering 1.1k
  • Information Systems 852
Replace Peilin Zhao with:
Peilin Zhao China
Bin Cui China
Bo Liu China
Zhang Xiong China
Guodong Long Australia
Cheng Yang China
Victor S. Sheng United States
Jing Gao United States
Shi‐Jinn Horng Taiwan
Ruili Wang New Zealand
Mao Ye relative to Peilin Zhao China Peilin Zhao's profile →
Citations per field
00.5×2.9×
Peilin Zhao · 1×
Citations per year

Countries citing papers authored by Mao Ye

Since Specialization
Citations

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

Fields of papers citing papers by Mao Ye

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mao Ye

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

All Works

20 of 20 papers shown
#WorkIndexed citations
1 1
2 0
3 0
4 0
5 10
6 8
7 8
8 1
9 6
10 44
11 6
12 1
13 3
14 20
15 23
16 1
17 2
18 1
19 0
20 44

About Mao Ye

Mao Ye is a scholar working on Computer Vision and Pattern Recognition, Media Technology and Signal Processing, having authored 318 papers that have together received 6.0k indexed citations. Recurring topics across this work include Video Surveillance and Tracking Methods (36 papers), Domain Adaptation and Few-Shot Learning (36 papers) and Advanced Neural Network Applications (32 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (2.1k citations), Transportation (672 citations) and Computer Networks and Communications (1.6k citations). Mao Ye has collaborated with scholars based in China, United States and Australia. Frequent co-authors include Wang-Chien Lee, Dik Lun Lee, Peifeng Yin, Juan Wu, Guihai Chen, Qihe Liu, Xiatian Zhu, Xue Li, Jie Wu and Xingjie Liu. Their work appears in journals such as Journal of Financial Economics, IEEE Transactions on Pattern Analysis and Machine Intelligence and Review of Financial Studies.

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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