Chao Lan

492 citations
43 papers · 325 · h-index 11

Impact in

Papers in

Chao Lan

39 papers receiving 317 citations

Peers

Chao Lan
Comparison fields: 5 of 67
  • Computer Vision and Pattern Recognition 148
  • Computational Mathematics 4
  • Signal Processing 72
  • Media Technology 44
  • Artificial Intelligence 151
Replace Chunyu Yang with:
Chunyu Yang China
Chaoqun Hong China
Chengyue Gong United States
Hai Huang China
Xiaochen Lian China
Rong Xiao China
Paulo Rauber Netherlands
Wanjun Chen China
Ming Xiang China
Chao Lan relative to Chunyu Yang China Chunyu Yang's profile →
Citations per field
00.5×1.5×2.0×
Chunyu Yang · 1×
Citations per year

Countries citing papers authored by Chao Lan

Since Specialization
Citations

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

Fields of papers citing papers by Chao Lan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 43 papers — load more, or switch the sort, to bring in the rest.

#Work
1 202248
2 201136
3 201423
4 201921
5 201716
6 201015
7 201213
8 201912
9 201912
10 201111
11 202011
12 201910
13 20209
14 20109
15 20188
16 20118
17 20127
18 20216
19 20215
20 20155

About Chao Lan

Chao Lan is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Signal Processing, Information Systems and Computational Mechanics, having authored 43 papers that have together received 325 indexed citations. Recurring topics across this work include Face and Expression Recognition (18 papers), Biometric Identification and Security (8 papers), Machine Learning and Data Classification (5 papers), Machine Learning and Algorithms (5 papers), Face recognition and analysis (5 papers), Domain Adaptation and Few-Shot Learning (4 papers), Ethics and Social Impacts of AI (4 papers) and Sparse and Compressive Sensing Techniques (4 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (148 citations), Computational Mathematics (4 citations), Signal Processing (72 citations), Media Technology (44 citations) and Artificial Intelligence (151 citations). Chao Lan has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include Xiao‐Yuan Jing, David Zhang, Sheng Li, Jun Huan, Jingyu Yang, Zhen Wang, Fei Wu, Yimu Ji, Qinghua Huang and Bo Luo. Their work appears in journals such as Information Sciences, Engineering Applications of Artificial Intelligence, Pattern Recognition, IEEE/ACM Transactions on Computational Biology and Bioinformatics and IEEE Transactions on Computational Social Systems.

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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