Xiaofeng Cao

460 total citations
23 papers, 275 citations indexed

About

Xiaofeng Cao is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Computer Networks and Communications. According to data from OpenAlex, Xiaofeng Cao has authored 23 papers receiving a total of 275 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Artificial Intelligence, 4 papers in Computer Vision and Pattern Recognition and 3 papers in Computer Networks and Communications. Recurrent topics in Xiaofeng Cao's work include Machine Learning and Algorithms (6 papers), Domain Adaptation and Few-Shot Learning (6 papers) and Advanced Graph Neural Networks (4 papers). Xiaofeng Cao is often cited by papers focused on Machine Learning and Algorithms (6 papers), Domain Adaptation and Few-Shot Learning (6 papers) and Advanced Graph Neural Networks (4 papers). Xiaofeng Cao collaborates with scholars based in China, Australia and Singapore. Xiaofeng Cao's co-authors include Guoyan Zheng, Zenglin Shi, Yun Liu, Yangdong Ye, Le Zhang, Ming‐Ming Cheng, Ivor W. Tsang, László B. Kish, Géza Pesti and Shirui Pan and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Optics Express and IEEE Communications Magazine.

In The Last Decade

Xiaofeng Cao

18 papers receiving 271 citations

Peers

Xiaofeng Cao
Comparison fields: 5 of 49
  • Artificial Intelligence 194
  • Computer Vision and Pattern Recognition 181
  • Safety, Risk, Reliability and Quality 42
  • Computer Networks and Communications 22
  • Transportation 22
Replace Biao Wang with:
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Wenzhe Zhai China
Tatsuo Kozakaya Japan
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Tarek Elguebaly Canada
Khalid Tahboub United States
Wei Qu China
Qingming Huang China
Shengqin Jiang China
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Citations per field, relative to Xiaofeng Cao
Xiaofeng Cao · 1×
Citations per year, relative to Xiaofeng Cao
Xiaofeng Cao · 1×

Countries citing papers authored by Xiaofeng Cao

Since Specialization
Citations

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

Fields of papers citing papers by Xiaofeng Cao

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Xiaofeng Cao

This figure shows the co-authorship network connecting the top 25 collaborators of Xiaofeng Cao. A scholar is included among the top collaborators of Xiaofeng Cao 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 Xiaofeng Cao. Xiaofeng Cao 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
# Work Indexed citations
1 0
2 0
3 0
4 1
5 0
6 4
7 1
8 1
9 0
10 12
11 4
12 2
13 4
14 1
15 4
16 2
17 4
18 200
19 19
20 2

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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