Xinyi Wu

52 total papers · 850 total citations
26 papers, 469 citations indexed

About

Xinyi Wu is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Ocean Engineering. According to data from OpenAlex, Xinyi Wu has authored 26 papers receiving a total of 469 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Computer Vision and Pattern Recognition, 4 papers in Artificial Intelligence and 3 papers in Ocean Engineering. Recurrent topics in Xinyi Wu's work include Image Enhancement Techniques (5 papers), Advanced Vision and Imaging (5 papers) and Advanced Neural Network Applications (3 papers). Xinyi Wu is often cited by papers focused on Image Enhancement Techniques (5 papers), Advanced Vision and Imaging (5 papers) and Advanced Neural Network Applications (3 papers). Xinyi Wu collaborates with scholars based in China and United States. Xinyi Wu's co-authors include Zhenyao Wu, Song Wang, Lili Ju, Hao Guo, Xiao–Ping Zhang, Hui Yin, Yang Mi, Jinglin Zhang, Erfan Goharian and Maohan Liang and has published in prestigious journals such as Nano Letters, IEEE Transactions on Pattern Analysis and Machine Intelligence and Optics Express.

In The Last Decade

Xinyi Wu

21 papers receiving 461 citations

Author Peers

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

Author Last Decade Papers Cites
Xinyi Wu 359 93 57 33 30 26 469
Songde Ma 352 1.0× 133 1.4× 64 1.1× 26 0.8× 19 0.6× 39 483
Nicolás Pérez de la Blanca 330 0.9× 143 1.5× 71 1.2× 51 1.5× 30 1.0× 40 525
Lanyun Zhu 293 0.8× 109 1.2× 44 0.8× 31 0.9× 34 1.1× 23 433
Yun Ren 347 1.0× 75 0.8× 116 2.0× 82 2.5× 28 0.9× 19 512
Aleksej Avramović 231 0.6× 52 0.6× 128 2.2× 38 1.2× 35 1.2× 23 414
Yujian Mo 240 0.7× 108 1.2× 64 1.1× 26 0.8× 23 0.8× 21 480
Mohammad Reza Ahmadzadeh 190 0.5× 127 1.4× 61 1.1× 34 1.0× 28 0.9× 38 489
Meng Ding 264 0.7× 59 0.6× 126 2.2× 63 1.9× 36 1.2× 19 436
Delong Chen 179 0.5× 110 1.2× 80 1.4× 17 0.5× 17 0.6× 25 500
Jun Miao 377 1.1× 73 0.8× 53 0.9× 49 1.5× 16 0.5× 28 496

Countries citing papers authored by Xinyi Wu

Since Specialization
Citations

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

Fields of papers citing papers by Xinyi Wu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Xinyi Wu

This figure shows the co-authorship network connecting the top 25 collaborators of Xinyi Wu. A scholar is included among the top collaborators of Xinyi Wu 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 Xinyi Wu. Xinyi Wu 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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