Xiaochen Yang

1.8k citations
70 papers · 1.2k · 2 hit papers · h-index 17

Impact in

Papers in

Xiaochen Yang

60 papers receiving 1.2k citations

Xiaochen Yang's Hit Papers

Deep metric learning for few-shot image classification: A Review of recent developments 2023 · 156 citations
1560+1+2Years since publication50100150

Peers

Xiaochen Yang
Comparison fields: 5 of 123
  • Inorganic Chemistry 181
  • Mechanical Engineering 432
  • Water Science and Technology 136
  • Computer Vision and Pattern Recognition 142
  • Process Chemistry and Technology 18
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Citations per field
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Citations per year

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 70 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 →
2023156
2
Heart-brain connections: Phenotypic and genetic insights from magnetic resonance images
Hit paper breakdown →
2023136
3 202092
4 202091
5 202082
6 202163
7 202059
8 202153
9 202051
10 202150
11 202441
12 202427
13 202223
14 202221
15 202220
16 202418
17 202018
18 202116
19 202413
20 202013

About Xiaochen Yang

Xiaochen Yang is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Mechanical Engineering, Media Technology and Finance, having authored 70 papers that have together received 1.2k indexed citations. Recurring topics across this work include Membrane Separation and Gas Transport (10 papers), Domain Adaptation and Few-Shot Learning (8 papers), Advanced Neural Network Applications (5 papers), Face and Expression Recognition (5 papers), Graphene research and applications (5 papers), Muon and positron interactions and applications (4 papers), Stochastic processes and financial applications (4 papers) and Multimodal Machine Learning Applications (4 papers). The work is most often cited by research in Inorganic Chemistry (181 citations), Mechanical Engineering (432 citations), Water Science and Technology (136 citations), Computer Vision and Pattern Recognition (142 citations) and Process Chemistry and Technology (18 citations). Xiaochen Yang has collaborated with scholars based in China, United Kingdom and United States. Frequent co-authors include Yan Feng Dai, Xiaoxu Li, Zhanyu Ma, Wenji Zheng, Jing-Hao Xue, Xuehua Ruan, Yuan Xi, Gaohong He, Xiaoming Yan and Weixin Guan. Their work appears in journals such as Pattern Recognition, Separation and Purification Technology, Journal of Membrane Science, IEEE Transactions on Circuits and Systems for Video Technology and Chemical Engineering Journal.

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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