@article{Tang2024C2RF,
	title={C2RF: Bridging Multi-modal Image Registration and Fusion via Commonality Mining and Contrastive Learning}, 
	author={Tang, Linfeng and Yan, Qinglong and Xiang, Xinyu and Fang, Leyuan and Ma, Jiayi},
	journal={International Journal of Computer Vision}, 
	year={2025},
  	volume={133},
  	pages={5262–5280},
}

• [2026-02-21] Our paper VideoFusion: A Spatio-Temporal Collaborative Network for Multi-modal Video Fusion of M3SVD has been officially accepted by The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2026)! [Paper] [Code]

• [2025-09-18] Our paper ControlFusion: A Controllable Image Fusion Framework with Language-Vision Degradation Prompts has been officially accepted by Advances in Neural Information Processing Systems (NeurIPS 2025)! [Paper] [Code]

• [2025-09-10] Our paper Mask-DiFuser: A Masked Diffusion Model for Unified Unsupervised Image Fusion has been officially accepted by IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE TPAMI)! [Paper] [Code]

• [2025-03-15] Our paper C2RF: Bridging Multi-modal Image Registration and Fusion via Commonality Mining and Contrastive Learning has been officially accepted by the International Journal of Computer Vision (IJCV)! [Paper] [Code]

• [2025-02-11] We released a large-scale dataset for infrared and visible video fusion: M3SVD: Multi-Modal Multi-Scene Video Dataset.

• torch 1.10.2+cu102
• torchvision 0.8.2
• kornia 0.5.2

The framework of the proposed C2RF for multi-modal image registration and fusion.

Please download the pretrained weights at the link below, and then place them into the folder ./checkpoint/

• The pretrained weights for the Roadscene dataset is at Google Drive.

• The pretrained weights for the PET-MRI dataset is at Google Drive.

python test.py --dataset=RoadScene 

python test.py --dataset=PET-MRI

python train_Fu.py --dataset=RoadScene

python train_Fu.py --dataset=PET-MRI

python train_Reg.py --dataset=RoadScene

python train_Reg.py --dataset=PET-MRI