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

• URL: http://arxiv.org/abs/2112.02991v1
• Date: Mon, 6 Dec 2021 13:12:36 GMT
• Title: Cross-Modality Attentive Feature Fusion for Object Detection in Multispectral Remote Sensing Imagery
• Authors: Qingyun Fang, Zhaokui Wang
• Abstract summary: Cross-modality fusing complementary information of multispectral remote sensing image pairs can improve the perception ability of detection algorithms. We propose a novel and lightweight multispectral feature fusion approach with joint common-modality and differential-modality attentions. Our proposed approach can achieve the state-of-the-art performance at a low cost.
• Score: 0.6853165736531939
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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