Accetped by IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), DOI: 10.1109/TPAMI.2023.3268209
Hui Li, Tianyang Xu, Xiao-Jun Wu*, Jiwen Lu, Josef Kittler
paper, Arxiv, Supplemental materials1, Supplemental materials2

Python 3.7
Pytorch >= 1.8

KAIST (S. Hwang, J. Park, N. Kim, Y. Choi, I. So Kweon, Multispectral pedestrian detection: Benchmark dataset and baseline, in: Proceedings of the IEEE conference on computer vision and pattern recognition, 2015, pp. 1037–1045.) is utilized to train LRRNet.

google drive

If you have any question about this code, feel free to reach me([email protected], [email protected])

@article{li2023lrrnet,
  title={{LRRNet: A novel representation learning guided fusion framework for infrared and visible images}},
  author={Li, Hui and Xu, Tianyang and Wu, Xiao-Jun and Lu, Jiwen and Kittler, Josef},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  volume={45},
  number={9},
  pages={11040-11052},
  year={2023},
  publisher={IEEE}
}