Research

Vision

My Fusion Intelligence Laboratory aims to use machine intelligence and multi-source information fusion to benefit humanity.

Research areas

My research areas include

• Computer vision (e.g., object tracking, human pose estimation, human behavior prediction)
• Deep learning (e.g., multi-modal learning, self-supervised learning, graph-based learning)
• Image fusion (visible-infrared, multi-focus, multi-exposure, multimodal medical images)
• Multimodal applications (e.g., RGB-T tracking, RGB-T segmentation, RGB-T crowd counting)
• Robotics and AI (e.g., Robot perception)
• Ethical AI (pedestrian privacy protection)
• AI for Science

Some funded projects

• Exeter-Fudan Fellowship
• The Royal Society Research Grant
• Marie-Curie Individual Fellowship
• Shanghai Science and Technology Committee Research Project

Research topics

1. Deep Learning-based image fusion: methods, benchmarks, and multi-modal applications

Related publications:

1. X. Zhang, Y. Demiris. Visible and Infrared Image Fusion using Deep Learning, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 8, pp. 10535-10554, 2023. (ESI Highly Cited Paper)
2. X. Zhang. Deep Learning-based Multi-focus Image Fusion: A Survey and A Comparative Study, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, No. 9, pp. 4819 – 4838, 2022. [Link] (ESI Highly Cited Paper)
3. X. Zhang. Benchmarking and Comparing Multi-exposure Image Fusion Algorithms. Information Fusion, vol. 74, pp. 111-131, 2021. (The first multi-exposure image fusion benchmark) [Benchmark link]
4. X. Zhang, P. Ye, H. Leung, K. Gong, G. Xiao. Object Fusion Tracking Based on Visible and Infrared Images: A Comprehensive Review. Information Fusion, vol. 63, pp. 166-187, 2020.
5. X. Zhang, P. Ye, G. Xiao. VIFB: A Visible and Infrared Image Fusion Benchmark, In the Proceedings of IEEE/CVF Conference on Computer Vision Workshops, 2020. (The first image fusion benchmark, which has been utilized by researchers from more than 10 countries.) [Benchmark link]
6. X. Zhang, P. Ye, S. Peng, J. Liu, G. Xiao. DSiamMFT: An RGB-T fusion tracking method via dynamic Siamese networks using multi-layer feature fusion. Signal Processing: Image Communication, vol. 84, 2020.
7. X. Zhang, P. Ye, D. Qiao, J. Zhao, S. Peng, G. Xiao. Object Fusion Tracking Based on Visible and Infrared Images Using Fully Convolutional Siamese Networks. In Proceedings of the 22nd International Conference on Information Fusion, 2019.
8. X. Zhang. “Multi-focus image fusion: A benchmark.” arXiv preprint arXiv:2005.01116 (2020). (The first multi-focus image fusion benchmark in the community)
9. Z. Zhao, A. Howes, X. Zhang*. MultiTaskVIF: Segmentation-oriented visible and infrared image fusion via multi-task learning. arXiv preprint arXiv:2505.06665. [Link]

2. Human-centered computer vision

(1) Pedestrian trajectory prediction based on graph neural network

(2) Pedestrian crossing intention prediction based on graph neural network

(3) Pedestrian tracking

(4) Crowd counting

Related publications:

1. X. Zhang*, P. Angeloudis, Y. Demiris. ST CrossingPose: A Spatial-Temporal Graph Convolutional Network for Skeleton-based Pedestrian Crossing Intention Prediction, IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 11, pp. 20773-20782, 2022.
2. X. Zhang*, P. Angeloudis, Y. Demiris. Dual-branch Spatio-Temporal Graph Neural Networks for Pedestrian Trajectory Prediction, Pattern Recognition, vol. 142, 2023.

* Corresponding authors

3. Responsible and ethical AI: Pedestrian privacy protection

Many videos are captured to train AI models. We aim to protect pedestrian privacy in videos captured by cameras mounted on robots and vehicles while maintaining the utility of the anonymized videos.

Related publications:

1. Z. Zhao, X. Zhang*, Y. Demiris. 3PFS: Protecting pedestrian privacy through face swapping, IEEE Transactions on Intelligent Transportation Systems, vol. 25, no. 11, pp. 16845-16854, 2024.

4. Computer vision

Related publications:

1. H. Li, Z. Wang, W. Kong, X. Zhang*. SelectMOT: Improving Data Association in Multiple Object Tracking via Quality-Aware Bounding Box Selection. IEEE Sensors Journal, 2025.
2. X. Zhang*, Y. Demiris. Self-Supervised RGB-T Tracking with Cross-Input Consistency. arXiv preprint arXiv:2301.11274 (2023).
3. J. Liu, P. Ye. X. Zhang*, G. Xiao. Real-time long-term tracking with reliability assessment and object recovery. IET Image Processing, vol. 15, no. 4, pp. 918-935, 2021.
4. J. Liu, G. Xiao, X. Zhang*, P. Ye, X. Xiong, S. Peng. Anti-occlusion object tracking based on correlation filter. Signal, Image and Video Processing, vol. 14, no. 4, pp. 753-761, 2020.
5. J. Zhao, G. Xiao*, X. Zhang*, D. P. Bavirisetti. An improved long-term correlation tracking method with occlusion handling. Chinese Optics Letters, vol. 17, no. 3, pp. 031001-1: 031001-6, 2019.

5. Robotics and AI, especially robot perception

6. AI, e.g., AI security, AI safety, AI for Social Good

Ongoing collaborative projects.

Related publications:

1. Y. Zhao, H. Wen, X. Zhang, M. Luo. BGM: Demand Prediction for Expanding Bike-Sharing Systems with Dynamic Graph Modeling, IJCAI2025.

7. AI for Science

Ongoing collaborative projects.