Aoran Xiao

1.9k total citations · 1 hit paper
29 papers, 1.1k citations indexed

About

Aoran Xiao is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Computational Mechanics. According to data from OpenAlex, Aoran Xiao has authored 29 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Computer Vision and Pattern Recognition, 12 papers in Artificial Intelligence and 8 papers in Computational Mechanics. Recurrent topics in Aoran Xiao's work include Domain Adaptation and Few-Shot Learning (12 papers), 3D Shape Modeling and Analysis (8 papers) and Advanced Neural Network Applications (7 papers). Aoran Xiao is often cited by papers focused on Domain Adaptation and Few-Shot Learning (12 papers), 3D Shape Modeling and Analysis (8 papers) and Advanced Neural Network Applications (7 papers). Aoran Xiao collaborates with scholars based in Singapore, China and Japan. Aoran Xiao's co-authors include Shijian Lu, Dayan Guan, Jiaxing Huang, Ling Shao, Jiaxing Huang, Xiaoqin Zhang, Yujin Chen, Dewen Wu, Ruizhi Chen and Yanpeng Cao and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Geoscience and Remote Sensing and Sensors.

In The Last Decade

Aoran Xiao

28 papers receiving 1.1k citations

Hit Papers

Unsupervised Point Cloud Representation Learning With Dee... 2023 2026 2024 2025 2023 25 50 75

Peers

Aoran Xiao
Comparison fields: 5 of 88
  • Computer Vision and Pattern Recognition 649
  • Artificial Intelligence 484
  • Computational Mechanics 154
  • Environmental Engineering 137
  • Aerospace Engineering 136
Replace Dayan Guan with:
Dayan Guan Singapore
Xuran Pan China
Xiaoyi Dong China
Marc Proesmans Belgium
Yawei Luo China
Jiaxing Huang Singapore
Ze Yang China
Songzhi Su China
Germán Ros Spain
Tao Guan China
Dayan Guan Singapore View profile →
Citations per field, relative to Aoran Xiao
Aoran Xiao · 1×
Citations per year, relative to Aoran Xiao
Aoran Xiao · 1×

Countries citing papers authored by Aoran Xiao

Since Specialization
Citations

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

Fields of papers citing papers by Aoran Xiao

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Aoran Xiao

This figure shows the co-authorship network connecting the top 25 collaborators of Aoran Xiao. A scholar is included among the top collaborators of Aoran Xiao 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 Aoran Xiao. Aoran Xiao 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 8
2 0
3 5
4 36
5 21
6 3
7 6
8 28
9 6
10
Unsupervised Point Cloud Representation Learning With Deep Neural Networks: A Survey breakdown →
84
11 10
12 8
13 106
14 38
15 27
16 55
17 30
18 170
19 49
20 1

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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