Fei Wu

5.6k total citations · 3 hit papers
188 papers, 4.2k citations indexed

About

Fei Wu is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Information Systems. According to data from OpenAlex, Fei Wu has authored 188 papers receiving a total of 4.2k indexed citations (citations by other indexed papers that have themselves been cited), including 103 papers in Computer Vision and Pattern Recognition, 51 papers in Artificial Intelligence and 32 papers in Information Systems. Recurrent topics in Fei Wu's work include Advanced Image and Video Retrieval Techniques (26 papers), Face and Expression Recognition (25 papers) and Video Surveillance and Tracking Methods (24 papers). Fei Wu is often cited by papers focused on Advanced Image and Video Retrieval Techniques (26 papers), Face and Expression Recognition (25 papers) and Video Surveillance and Tracking Methods (24 papers). Fei Wu collaborates with scholars based in China, United States and Japan. Fei Wu's co-authors include Xiao‐Yuan Jing, Da Chen, Ke Fan, Chaohua Jiang, Baowen Xu, Xiwei Dong, Yueting Zhuang, Ruimin Hu, Yimu Ji and Xiaoke Zhu and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Journal of Bacteriology and IEEE Transactions on Image Processing.

In The Last Decade

Fei Wu

169 papers receiving 4.1k 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.

Experimental study on the mechanical properties and microstructure of chopped basalt fibre reinforced concrete

2014 570 citations Fei Wu et al. profile →
Deep discriminative transfer learning network for cross-machine fault diagnosis

2022 249 citations Quan Qian, Yi Qin et al. profile →
DF-GAN: A Simple and Effective Baseline for Text-to-Image Synthesis

2022 155 citations Fei Wu, Xiao‐Yuan Jing et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Fei Wu China	29	1.9k	972	665	586	500	188	4.2k
Li Jiang China	30	3.7k 1.9×	997 1.0×	318 0.5×	130 0.2×	158 0.3×	126	7.2k
Kunfeng Wang China	27	1.6k 0.8×	710 0.7×	137 0.2×	121 0.2×	48 0.1×	108	4.1k
Jiachen Yang China	40	2.1k 1.1×	761 0.8×	522 0.8×	61 0.1×	42 0.1×	221	5.1k
Yuanyan Tang China	25	1.2k 0.6×	723 0.7×	310 0.5×	79 0.1×	179 0.4×	125	3.1k
Bin Fang China	31	2.1k 1.1×	604 0.6×	350 0.5×	54 0.1×	187 0.4×	304	3.7k
Fazhi He China	34	1.4k 0.7×	701 0.7×	222 0.3×	42 0.1×	46 0.1×	120	3.2k
Song Han United States	31	4.1k 2.1×	2.8k 2.9×	180 0.3×	189 0.3×	27 0.1×	104	7.1k
Yong Guan China	29	631 0.3×	676 0.7×	199 0.3×	84 0.1×	83 0.2×	211	3.8k
Ge Li China	31	2.7k 1.4×	1.7k 1.8×	630 0.9×	27 0.0×	269 0.5×	257	5.1k

Countries citing papers authored by Fei Wu

Since Specialization

Citations

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

Fields of papers citing papers by Fei Wu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Fei Wu

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

20 of 20 papers shown

Zheng, Wei, et al.. (2025). Cluster-graph convolution networks for robust multi-view clustering. Knowledge-Based Systems. 327. 114163–114163.

Wu, Fei, et al.. (2025). Two-Stage Knowledge Distillation Network With Multi-Teacher Self-Training for Semi-Supervised Multi-Modal Fake News Detection. IEEE Transactions on Network Science and Engineering. 13. 639–654.

Wu, Fei, et al.. (2025). An Active Defense Adjudication Method Based on Adaptive Anomaly Sensing for Mimic IoT. IEEE Transactions on Services Computing. 18(1). 57–71. 2 indexed citations

Liu, Chunying, Guangwei Gao, Fei Wu, Zhenhua Guo, & Yi Yu. (2024). An efficient feature reuse distillation network for lightweight image super-resolution. Computer Vision and Image Understanding. 249. 104178–104178. 1 indexed citations

Hu, Changhui, et al.. (2024). Degrade for upgrade: Learning degradation representations for real-world low-light image enhancement. Computers & Electrical Engineering. 119. 109622–109622. 10 indexed citations

Liu, Shangdong, et al.. (2024). Mimic turbo compiled code structure for wireless communication systems. IET Communications. 18(17). 1089–1106.

Ji, Yimu, et al.. (2024). Homogeneous and heterogeneous relational graph for visible-infrared person re-identification. Pattern Recognition. 158. 110981–110981. 6 indexed citations

Qian, Quan, Fei Wu, Yi Wang, & Yi Qin. (2024). Maximum subspace transferability discriminant analysis: A new cross-domain similarity measure for wind-turbine fault transfer diagnosis. Computers in Industry. 164. 104194–104194. 7 indexed citations

Hu, Changhui, Yin Hu, Ziyun Cai, et al.. (2024). Multiscale hybrid feature guided normalizing flow for low-light image enhancement. Computers & Electrical Engineering. 122. 109922–109922. 1 indexed citations

10.

Yuan, Quan, Yuhang Yang, Fei Wu, et al.. (2024). Planar Large Field‐of‐View Spectral Imaging Based on Metasurface Array. Advanced Optical Materials. 13(5). 1 indexed citations

11.

Jing, Xiao‐Yuan, et al.. (2023). Task-specific parameter decoupling for class incremental learning. Information Sciences. 651. 119731–119731. 5 indexed citations

12.

Wu, Fei, Tingting Zhang, Xue Li, et al.. (2023). Complete genome sequence and comparative analysis of a Vibrio vulnificus strain isolated from a clinical patient. Frontiers in Microbiology. 14. 1240835–1240835. 3 indexed citations

13.

Gao, Guangwei, Lei Tang, Fei Wu, Huimin Lu, & Jian Yang. (2023). JDSR-GAN: Constructing an Efficient Joint Learning Network for Masked Face Super-Resolution. IEEE Transactions on Multimedia. 25. 1505–1512. 14 indexed citations

14.

Jing, Xiao‐Yuan, Xinyu Zhang, Xiaoke Zhu, et al.. (2019). Multiset Feature Learning for Highly Imbalanced Data Classification. IEEE Transactions on Pattern Analysis and Machine Intelligence. 43(1). 139–156. 106 indexed citations

15.

Zhang, Zhiwu, Xiao‐Yuan Jing, & Fei Wu. (2018). Low‐rank representation for semi‐supervised software defect prediction. IET Software. 12(6). 527–535. 8 indexed citations

16.

Dong, Xiwei, Fei Wu, & Xiao‐Yuan Jing. (2018). Semi-supervised multiple kernel intact discriminant space learning for image recognition. Neural Computing and Applications. 31(9). 5309–5326. 6 indexed citations

17.

Wu, Fei, Xiao‐Yuan Jing, Xiwei Dong, et al.. (2018). Intraspectrum Discrimination and Interspectrum Correlation Analysis Deep Network for Multispectral Face Recognition. IEEE Transactions on Cybernetics. 50(3). 1009–1022. 28 indexed citations

18.

Dong, Xiwei, Fei Wu, Xiao‐Yuan Jing, & Songsong Wu. (2018). Multi-view Intact Discriminant Space Learning for Image Classification. Neural Processing Letters. 50(2). 1661–1685. 4 indexed citations

19.

Yang, Ye, Yongli Hu, & Fei Wu. (2018). Sparse and Low-Rank Subspace Data Clustering with Manifold Regularization Learned by Local Linear Embedding. Applied Sciences. 8(11). 2175–2175. 5 indexed citations

20.

Wu, Fei, Yongli Hu, Junbin Gao, Yanfeng Sun, & Baocai Yin. (2015). Ordered Subspace Clustering With Block-Diagonal Priors. IEEE Transactions on Cybernetics. 46(12). 3209–3219. 27 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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