Zhenyu Wu

1.3k total citations · 1 hit paper
10 papers, 765 citations indexed

About

Zhenyu Wu is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Management Science and Operations Research. According to data from OpenAlex, Zhenyu Wu has authored 10 papers receiving a total of 765 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Artificial Intelligence, 3 papers in Computer Vision and Pattern Recognition and 3 papers in Management Science and Operations Research. Recurrent topics in Zhenyu Wu's work include Data Quality and Management (2 papers), Data Stream Mining Techniques (2 papers) and Advanced Graph Neural Networks (2 papers). Zhenyu Wu is often cited by papers focused on Data Quality and Management (2 papers), Data Stream Mining Techniques (2 papers) and Advanced Graph Neural Networks (2 papers). Zhenyu Wu collaborates with scholars based in China, United States and United Kingdom. Zhenyu Wu's co-authors include Richard M. Leahy, Yang Ji, Norman W. Paton, Liqiang Song, Jing Xiao, Jiaoyan Chen, Chunhong Zhang, Aimin Hao, Xuanhua Shi and Qing Xia and has published in prestigious journals such as PLoS ONE, IEEE Transactions on Pattern Analysis and Machine Intelligence and ACM Computing Surveys.

In The Last Decade

Zhenyu Wu

10 papers receiving 695 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

An optimal graph theoretic approach to data clustering: theory and its application to image segmentation

1993 716 citations Zhenyu Wu, Richard M. Leahy IEEE Transactions on Pattern Analysis and Machine Intelligence profile →

Peers

Zhenyu Wu

CVPR

AI

MT

SNP

SP

Yuanman Li Macao

Chengzhi Deng China

Grigorios Tzortzis Greece

Xiaosheng Zhuang Hong Kong

Neveen I. Ghali Egypt

Caiming Zhong China

Weizhong Yu China

Askar Hamdulla China

Yuanman Li Macao

Zhenyu Wu

956 ×2.0

CVPR

297 ×1.2

AI

148 ×1.4

MT

81 ×1.3

SNP

65 ×1.2

SP

Citations per year, relative to Zhenyu Wu Zhenyu Wu (= 1×) peers Yuanman Li

Countries citing papers authored by Zhenyu Wu

Since Specialization

Citations

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

Fields of papers citing papers by Zhenyu Wu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Zhenyu Wu

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

All Works

Sort: Min cites: Since: Top N: Style:

10 of 10 papers shown

1.

Wu, Zhenyu, et al.. (2024). Large Language Models Can Self-Correct with Key Condition Verification. 12846–12867. 2 indexed citations

2.

Wu, Zhenyu, et al.. (2024). Decoupled Federated Learning on Long-Tailed and Non-IID data with Feature Statistics. 1–6. 2 indexed citations

3.

Wu, Zhenyu, Lin Wang, Wei Wang, et al.. (2023). Pixel Is All You Need: Adversarial Trajectory-Ensemble Active Learning for Salient Object Detection. Proceedings of the AAAI Conference on Artificial Intelligence. 37(3). 2883–2891. 4 indexed citations

4.

Wu, Zhenyu, et al.. (2023). Data Stream Clustering: An In-depth Empirical Study. Proceedings of the ACM on Management of Data. 1(2). 1–26. 5 indexed citations

5.

Paton, Norman W., Jiaoyan Chen, & Zhenyu Wu. (2023). Dataset Discovery and Exploration: A Survey. ACM Computing Surveys. 56(4). 1–37. 7 indexed citations

6.

Zhang, Chunhong, et al.. (2022). A survival analysis based volatility and sparsity modeling network for student dropout prediction. PLoS ONE. 17(5). e0267138–e0267138. 11 indexed citations

7.

Dong, Qi, et al.. (2022). Existence and practice of gaming: thoughts on the development of multi-agent system gaming. Frontiers of Information Technology & Electronic Engineering. 23(7). 995–1001. 3 indexed citations

8.

Song, Liqiang, et al.. (2020). DREAM. 2225–2228. 14 indexed citations

9.

Mao, Kang, et al.. (2019). A Heterogeneous Conversational Recommender System for Financial Products.. 26–30. 1 indexed citations

10.

Wu, Zhenyu & Richard M. Leahy. (1993). An optimal graph theoretic approach to data clustering: theory and its application to image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence. 15(11). 1101–1113. 716 indexed citations breakdown →

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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