Chenshen Wu

728 total citations
4 papers, 186 citations indexed

About

Chenshen Wu is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Infectious Diseases. According to data from OpenAlex, Chenshen Wu has authored 4 papers receiving a total of 186 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Computer Vision and Pattern Recognition, 4 papers in Artificial Intelligence and 0 papers in Infectious Diseases. Recurrent topics in Chenshen Wu's work include Domain Adaptation and Few-Shot Learning (4 papers), Multimodal Machine Learning Applications (2 papers) and Generative Adversarial Networks and Image Synthesis (2 papers). Chenshen Wu is often cited by papers focused on Domain Adaptation and Few-Shot Learning (4 papers), Multimodal Machine Learning Applications (2 papers) and Generative Adversarial Networks and Image Synthesis (2 papers). Chenshen Wu collaborates with scholars based in Spain, China and Sweden. Chenshen Wu's co-authors include Joost van de Weijer, Luis Herranz, Xialei Liu, Bogdan Raducanu, Shangling Jui, Bogdan Raducanu, Andrew D. Bagdanov, Yaxing Wang, Fahad Shahbaz Khan and Abel González-García and has published in prestigious journals such as International Journal of Computer Vision, Florence Research (University of Florence) and Neural Information Processing Systems.

In The Last Decade

Chenshen Wu

4 papers receiving 182 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Chenshen Wu Spain	3	164	133	18	8	6	4	186
Arslan Chaudhry United States	4	192 1.2×	136 1.0×	22 1.2×	8 1.0×	3 0.5×	9	213
Arthur Douillard France	4	185 1.1×	137 1.0×	20 1.1×	5 0.6×	3 0.5×	7	214
Gabriele Graffieti Italy	6	93 0.6×	72 0.5×	13 0.7×	6 0.8×	12 2.0×	9	133
Anastasia Pentina Austria	4	160 1.0×	109 0.8×	14 0.8×	4 0.5×	7 1.2×	5	205
Soravit Changpinyo United States	9	123 0.8×	147 1.1×	16 0.9×	2 0.3×	6 1.0×	16	183
Paola Cascante-Bonilla United States	6	109 0.7×	95 0.7×	9 0.5×	5 0.6×	4 0.7×	8	157
Liangchen Luo China	4	134 0.8×	53 0.4×	5 0.3×	4 0.5×	7 1.2×	7	177
Marcin Moczulski United Kingdom	3	81 0.5×	94 0.7×	5 0.3×	11 1.4×	8 1.3×	5	127
Matteo Stefanini Italy	5	108 0.7×	209 1.6×	11 0.6×	3 0.4×	5 0.8×	6	255
Xuehai He United States	6	68 0.4×	52 0.4×	16 0.9×	6 0.8×	4 0.7×	13	103

Countries citing papers authored by Chenshen Wu

Since Specialization

Citations

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

Fields of papers citing papers by Chenshen Wu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Chenshen Wu

This figure shows the co-authorship network connecting the top 25 collaborators of Chenshen Wu. A scholar is included among the top collaborators of Chenshen 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 Chenshen Wu. Chenshen 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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4 of 4 papers shown

1.

Wang, Yaxing, Abel González-García, Chenshen Wu, et al.. (2023). MineGAN++: Mining Generative Models for Efficient Knowledge Transfer to Limited Data Domains. International Journal of Computer Vision. 132(2). 490–514. 2 indexed citations

2.

Wu, Chenshen & Joost van de Weijer. (2023). Density Map Distillation for Incremental Object Counting. 114. 2506–2515. 2 indexed citations

3.

Liu, Xialei, Chenshen Wu, Luis Herranz, et al.. (2020). Generative Feature Replay For Class-Incremental Learning. Florence Research (University of Florence). 915–924. 67 indexed citations

4.

Wu, Chenshen, et al.. (2018). Memory Replay GANs: Learning to Generate New Categories without Forgetting. Neural Information Processing Systems. 31. 5962–5972. 115 indexed citations

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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