Xiao‐Jun Wu

14.4k total citations · 6 hit papers
435 papers, 8.3k citations indexed

About

Xiao‐Jun Wu is a scholar working on Computer Vision and Pattern Recognition, Media Technology and Artificial Intelligence. According to data from OpenAlex, Xiao‐Jun Wu has authored 435 papers receiving a total of 8.3k indexed citations (citations by other indexed papers that have themselves been cited), including 318 papers in Computer Vision and Pattern Recognition, 122 papers in Media Technology and 95 papers in Artificial Intelligence. Recurrent topics in Xiao‐Jun Wu's work include Face and Expression Recognition (113 papers), Advanced Image Fusion Techniques (75 papers) and Video Surveillance and Tracking Methods (74 papers). Xiao‐Jun Wu is often cited by papers focused on Face and Expression Recognition (113 papers), Advanced Image Fusion Techniques (75 papers) and Video Surveillance and Tracking Methods (74 papers). Xiao‐Jun Wu collaborates with scholars based in China, United Kingdom and United States. Xiao‐Jun Wu's co-authors include Josef Kittler, Hui Li, Tianyang Xu, Jun Sun, Wei Fang, Zhenhua Feng, Vasile Palade, Xiaoqing Luo, Zhancheng Zhang and Wenbo Xu and has published in prestigious journals such as SHILAP Revista de lepidopterología, Bioinformatics and PLoS ONE.

In The Last Decade

Xiao‐Jun Wu

397 papers receiving 8.1k citations

Hit Papers

RFN-Nest: An end-to-end residual fusion network for in... 2018 2026 2020 2023 2021 2020 2018 2023 2023 200 400 600

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Xiao‐Jun Wu China 43 4.8k 3.0k 1.5k 1.3k 584 435 8.3k
Qiguang Miao China 40 2.4k 0.5× 1.7k 0.6× 1.3k 0.9× 571 0.4× 553 0.9× 246 5.8k
Chenggang Yan China 45 6.2k 1.3× 954 0.3× 1.5k 1.0× 607 0.5× 960 1.6× 259 10.0k
Junchi Yan China 44 5.0k 1.0× 1.3k 0.4× 2.4k 1.6× 1.5k 1.2× 183 0.3× 267 8.7k
Chang Xu China 48 7.5k 1.6× 1.7k 0.6× 4.2k 2.7× 627 0.5× 704 1.2× 266 12.3k
Xiaolin Hu China 37 5.4k 1.1× 893 0.3× 2.3k 1.5× 1.1k 0.8× 452 0.8× 146 8.7k
Wengang Zhou China 49 8.0k 1.7× 1.3k 0.4× 1.5k 1.0× 1.2k 1.0× 1.5k 2.6× 287 10.3k
Fatih Porikli Australia 54 10.5k 2.2× 2.0k 0.7× 2.4k 1.6× 1.0k 0.8× 543 0.9× 240 12.6k
Shiming Xiang China 48 5.8k 1.2× 2.7k 0.9× 2.2k 1.4× 466 0.4× 319 0.5× 232 9.4k
Yunhe Wang China 29 5.0k 1.0× 1.3k 0.4× 2.0k 1.3× 571 0.4× 376 0.6× 88 7.4k
Xiang Bai China 65 15.0k 3.1× 4.7k 1.6× 3.3k 2.2× 1.8k 1.4× 665 1.1× 260 18.0k

Countries citing papers authored by Xiao‐Jun Wu

Since Specialization
Citations

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

Fields of papers citing papers by Xiao‐Jun Wu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Xiao‐Jun Wu

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

All Works

20 of 20 papers shown
1.
Zhang, Peihong, et al.. (2025). UQ4CFD: An Uncertainty Quantification Platform for CFD Simulation. Aerospace. 12(10). 886–886.
2.
Xu, Tianyang, et al.. (2024). DDBFusion: An unified image decomposition and fusion framework based on dual decomposition and Bézier curves. Information Fusion. 114. 102655–102655. 17 indexed citations
3.
Feng, Zhenhua, et al.. (2024). MMDG-DTI: Drug–target interaction prediction via multimodal feature fusion and domain generalization. Pattern Recognition. 157. 110887–110887. 16 indexed citations
4.
Zhu, Xuefeng, et al.. (2024). Self-supervised learning for RGB-D object tracking. Pattern Recognition. 155. 110543–110543. 6 indexed citations
5.
Li, Hui, et al.. (2024). Conti-Fuse: A novel continuous decomposition-based fusion framework for infrared and visible images. Information Fusion. 117. 102839–102839. 8 indexed citations
6.
Xu, Tianyang, et al.. (2024). M-adapter: Multi-level image-to-video adaptation for video action recognition. Computer Vision and Image Understanding. 249. 104150–104150. 2 indexed citations
7.
Xu, Tianyang, Yunjie Zhang, Xiaoning Song, & Xiao‐Jun Wu. (2024). Towards fine-grained adaptive video captioning via Quality-Aware Recurrent Feedback Network. Expert Systems with Applications. 261. 125480–125480. 1 indexed citations
8.
Yue, Song, et al.. (2024). Adaptive Log-Euclidean Metrics for SPD Matrix Learning. IEEE Transactions on Image Processing. 33. 5194–5205. 1 indexed citations
9.
Xu, Tianyang, et al.. (2024). Learning Feature Restoration Transformer for Robust Dehazing Visual Object Tracking. International Journal of Computer Vision. 132(12). 6021–6038. 1 indexed citations
10.
Wu, Cong, Xiao‐Jun Wu, Tianyang Xu, & Josef Kittler. (2023). Scene adaptive mechanism for action recognition. Computer Vision and Image Understanding. 238. 103854–103854. 4 indexed citations
11.
Shu, Zhenqiu, et al.. (2023). Robust online hashing with label semantic enhancement for cross-modal retrieval. Pattern Recognition. 145. 109972–109972. 32 indexed citations
12.
Luo, Xiaoqing, Yuting Jiang, Anqi Wang, et al.. (2023). Infrared and visible image fusion based on Multi-State contextual hidden Markov Model. Pattern Recognition. 138. 109431–109431. 11 indexed citations
13.
Xu, Tianyang, Xuefeng Zhu, & Xiao‐Jun Wu. (2023). Learning spatio-temporal discriminative model for affine subspace based visual object tracking. 1(1). 16 indexed citations
14.
Tang, Zhangyong, Tianyang Xu, Hui Li, et al.. (2023). Exploring fusion strategies for accurate RGBT visual object tracking. Information Fusion. 99. 101881–101881. 59 indexed citations
15.
Zang, Ying, et al.. (2023). Joint dual-stream interaction and multi-scale feature extraction network for multi-spectral pedestrian detection. Applied Soft Computing. 147. 110768–110768. 5 indexed citations
16.
Luo, Xiaoqing, et al.. (2023). Infrared and visible image fusion based on quaternion wavelets transform and feature-level Copula model. Multimedia Tools and Applications. 83(10). 28549–28577. 5 indexed citations
17.
Yu, Wanrong, et al.. (2023). Scalable Affine Multi-view Subspace Clustering. Neural Processing Letters. 55(4). 4679–4696. 1 indexed citations
18.
Li, Hui & Xiao‐Jun Wu. (2023). CrossFuse: A novel cross attention mechanism based infrared and visible image fusion approach. Information Fusion. 103. 102147–102147. 137 indexed citations breakdown →
19.
Dong, Bei, et al.. (2023). Adaptive stochastic fractal search algorithm for multi-objective optimization. Swarm and Evolutionary Computation. 83. 101392–101392. 1 indexed citations
20.
Wu, Xiao‐Jun, et al.. (2020). Two-stream face spoofing detection network combined with hybrid pooling. Journal of Image and Graphics. 25(7). 1408–1420. 1 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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