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Cross-Modality Attentive Feature Fusion for Object Detection in Multispectral Remote Sensing Imagery

Auhtor: FANG Qingyun and WANG Zhaokui

Intro

CMAFF:Cross-Modality Attentive Feature Fusion

Differential Enhancive Module

Common Selective Module

Cross-modality fusing complementary information of multispectral remote sensing image pairs can improve the perception ability of detection algorithms, making them more robust and reliable for a wider range of applications, such as nighttime detection. Compared with prior methods, we think different features should be processed specifically, the modality-specific features should be retained and enhanced, while the modality-shared features should be cherry- picked from the RGB and thermal IR modalities. Following this idea, a novel and lightweight multispectral feature fusion approach with joint common-modality and differential-modality attentions are proposed, named Cross-Modality Attentive Feature Fusion (CMAFF). Given the intermediate feature maps of RGB and IR images, our module parallel infers attention maps from two separate modalities, common- and differential-modality, then the attention maps are multiplied to the input feature map respectively for adaptive feature enhancement or selection. Extensive experiments demonstrate that our proposed approach can achieve the state-of-the-art performance at a low computation cost. For more details, please refer to our paper.

Citation

If you are interested this repo for your research, welcome to cite our paper:

@article{qingyun2022cross,
  title={Cross-Modality Attentive Feature Fusion for Object Detection in Multispectral Remote Sensing Imagery},
  author={Qingyun, Fang and Zhaokui, Wang},
  journal={Pattern Recognition},
  pages={108786},
  year={2022},
  publisher={Elsevier}
}

Result