Xiaochen Yang

1.6k citations
62 papers · 982 indexed · 2 hit papers · h-index 15

Impact in

Papers in

Xiaochen Yang

54 papers receiving 968 citations

Hit Papers

Deep metric learning for few-shot image classification: A Review of recent developments 2023 · 120 citations
1200+1+2Years since publication4080120

Peers

Xiaochen Yang
Comparison fields: 5 of 118
  • Inorganic Chemistry 172
  • Mechanical Engineering 400
  • Water Science and Technology 128
  • Process Chemistry and Technology 17
  • Computer Vision and Pattern Recognition 117
Replace Guoqing Li with:
Guoqing Li China
Hongsong Li China
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Haiyan Jiang China
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Woosung Choi South Korea
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Citations per field
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Countries citing papers authored by Xiaochen Yang

Since Specialization
Citations

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

Fields of papers citing papers by Xiaochen Yang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 62 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Deep metric learning for few-shot image classification: A Review of recent developments
Hit paper breakdown →
2023120
2
Heart-brain connections: Phenotypic and genetic insights from magnetic resonance images
Hit paper breakdown →
202399
3 202091
4 202087
5 202079
6 202160
7 202051
8 202147
9 202143
10 202042
11 202425
12 202218
13 202016
14 202415
15 202214
16 202213
17 201912
18 202411
19 202411
20 202011

About Xiaochen Yang

Xiaochen Yang is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Mechanical Engineering, Materials Chemistry and Biomedical Engineering, having authored 62 papers that have together received 982 indexed citations. Recurring topics across this work include Membrane Separation and Gas Transport (10 papers), Domain Adaptation and Few-Shot Learning (9 papers), Graphene research and applications (5 papers), Face and Expression Recognition (4 papers), Stochastic processes and financial applications (4 papers), Advanced Neural Network Applications (4 papers), Muon and positron interactions and applications (4 papers) and Multimodal Machine Learning Applications (4 papers). The work is most often cited by research in Inorganic Chemistry (172 citations), Mechanical Engineering (400 citations), Water Science and Technology (128 citations), Process Chemistry and Technology (17 citations) and Computer Vision and Pattern Recognition (117 citations). Xiaochen Yang has collaborated with scholars based in China, United Kingdom and United States. Frequent co-authors include Yan Dai, Wenji Zheng, Zhanyu Ma, Xiaoxu Li, Yuan Xi, Xuehua Ruan, Gaohong He, Weixin Guan, Xiaoming Yan and Chen‐Yuan Dong. Their work appears in journals such as Journal of Membrane Science, Separation and Purification Technology, Pattern Recognition, Chemical Engineering Journal and Neurocomputing.

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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