Xiaojun Wu
Also known as Xiao-Jun Wu, Wu Xiaojun, 吴小俊
Xiaojun Wu is a professor of pattern recognition at Jiangnan University and dean of its Graduate School. He earned a B.S. in mathematics at Nanjing Normal University in 1991 and a Ph.D. in pattern recognition and intelligent system at Nanjing University of Science and Technology in 2002. From 2003 to 2004 he held a visiting postdoctoral post under Josef Kittler at the University of Surrey, and in 2012 he coauthored a convergence analysis of quantum-behaved particle swarm optimization. With Hui Li, he presented DenseFuse in 2018, a deep architecture for fusing infrared and visible images. In 2022 the International Association for Pattern Recognition named him a fellow for image and video representation, image and multimodal fusion, and particle swarm optimization.
- Now at
- Jiangnan University
- Works
- 690
- Citations
- 15,611
- h-index
- 57
Publication numbers from OpenAlex, as of 17 October 2026.
Timeline
-
1991–2006
Degrees and early posts
He receives a B.S. in mathematics from Nanjing Normal University in 1991, an M.S. in 1996, and a Ph.D. in pattern recognition and intelligent system in 2002, the latter two from Nanjing University of Science and Technology. From 1996 to 2006 he teaches in the School of Electronics and Information at Jiangsu University of Science and Technology, where he is an exceptionally promoted professor, and from 1999 to 2000 he is a fellow of the United Nations University International Institute for Software Technology. From 2003 to 2004 he is a visiting postdoctoral researcher in the Centre for Vision, Speech, and Signal Processing at the University of Surrey under Josef Kittler.
-
2006
Joining Jiangnan University
He joins Jiangnan University in 2006, where he is a distinguished professor in the School of Artificial Intelligence and Computer Science.
-
2012
A swarm convergence paper
In 2012, with Jun Sun, Vasile Palade, Wei Fang, Choi-Hong Lai, and Wenbo Xu, he publishes a convergence analysis of quantum-behaved particle swarm optimization in Information Sciences.
-
2018
DenseFuse for image fusion
In 2018, with Hui Li, he presents DenseFuse, a deep learning architecture that fuses infrared and visible images with convolutional layers, a fusion layer, and dense blocks.
-
2022
An IAPR Fellowship
In 2022 the International Association for Pattern Recognition names him a fellow for contributions to image and video representation, image and multimodal fusion, and particle swarm optimization for pattern recognition.
-
2022
Laboratories and published work
In 2022 he directs the Jiangsu Provincial Joint Laboratory for International Cooperation in Artificial Intelligence and the Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence. He has published more than 400 academic papers and 5 academic books, and he chairs the National Excellence Course Introduction to Artificial Intelligence.
-
April 2026
EvaNet for fusion scores
In April 2026, with Chunyang Cheng, Tianyang Xu, Hui Li, and Josef Kittler, he coauthors EvaNet, a lightweight network for evaluating infrared and visible image fusion.
-
As of October 2026
Dean of the graduate school
He is dean of the Graduate School at Jiangnan University, which he joined in 2006, and a distinguished professor of pattern recognition and computational intelligence. He directs the Josef Kittler Artificial Intelligence Institute and the Ministry of Science and Technology China-UK joint laboratory on artificial intelligence.
Sources and credits
Photo: We have no published photo of Xiaojun Wu yet. Send us a free-licensed photo.
- 吴小俊-江南大学人工智能与计算机学院（软件学院）
- Greenwich Academic Literature Archive - Items where Author is "Wu, Xiaojun"
- [1804.08361] DenseFuse: A Fusion Approach to Infrared and Visible Images
- Chronological List of IAPR Fellows – International Association for Pattern Recognition
- Prof Wu Xiaojun from the School of Artificial Intelligence and Computer Science Being Named as A New Fellow of the IAPR in 2022-Jiangnan University
- [2604.02896] EvaNet: Towards More Efficient and Consistent Infrared and Visible Image Fusion Assessment