Guangyao Chen

23 total papers · 930 total citations
10 papers, 377 citations indexed

About

Guangyao Chen is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Media Technology. According to data from OpenAlex, Guangyao Chen has authored 10 papers receiving a total of 377 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Artificial Intelligence, 4 papers in Computer Vision and Pattern Recognition and 2 papers in Media Technology. Recurrent topics in Guangyao Chen's work include Domain Adaptation and Few-Shot Learning (4 papers), Anomaly Detection Techniques and Applications (4 papers) and Multimodal Machine Learning Applications (2 papers). Guangyao Chen is often cited by papers focused on Domain Adaptation and Few-Shot Learning (4 papers), Anomaly Detection Techniques and Applications (4 papers) and Multimodal Machine Learning Applications (2 papers). Guangyao Chen collaborates with scholars based in China and United States. Guangyao Chen's co-authors include Yonghong Tian, Peixi Peng, Xiangqian Wang, Wen Gao, Xiao-Yong Wei, Pengcheng Gao, Yaowei Wang, Xiao Wang, Li Ma and Li Jia and has published in prestigious journals such as Advanced Materials, IEEE Transactions on Pattern Analysis and Machine Intelligence and IEEE Transactions on Circuits and Systems for Video Technology.

In The Last Decade

Guangyao Chen

9 papers receiving 369 citations

Hit Papers

Adversarial Reciprocal Po... 2021 2026 2022 2024 2021 2023 50 100 150

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Guangyao Chen 231 136 32 26 26 10 377
Jyoti Prakash Sahoo 165 0.7× 82 0.6× 22 0.7× 20 0.8× 15 0.6× 14 341
Bo Li 175 0.8× 188 1.4× 33 1.0× 21 0.8× 33 1.3× 15 393
Ruijia Xu 224 1.0× 175 1.3× 22 0.7× 28 1.1× 16 0.6× 9 348
Chunjiang Ge 148 0.6× 248 1.8× 59 1.8× 22 0.8× 41 1.6× 7 442
Yaroslav Ganin 202 0.9× 160 1.2× 20 0.6× 45 1.7× 14 0.5× 5 377
Zhichao Song 166 0.7× 195 1.4× 27 0.8× 37 1.4× 18 0.7× 19 403
Pedro Ribeiro Mendes Júnior 178 0.8× 102 0.8× 26 0.8× 32 1.2× 16 0.6× 18 350
Saleh Albelwi 149 0.6× 144 1.1× 27 0.8× 23 0.9× 11 0.4× 15 407
Jiun-Wei Liou 131 0.6× 119 0.9× 56 1.8× 34 1.3× 17 0.7× 8 432
Mary M. Moya 246 1.1× 78 0.6× 42 1.3× 40 1.5× 40 1.5× 18 381

Countries citing papers authored by Guangyao Chen

Since Specialization
Citations

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

Fields of papers citing papers by Guangyao Chen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Guangyao Chen

This figure shows the co-authorship network connecting the top 25 collaborators of Guangyao Chen. A scholar is included among the top collaborators of Guangyao Chen 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 Guangyao Chen. Guangyao Chen is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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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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