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Cross-modality fusing complementary information of multispectral remotesensing image pairs can improve the perception ability of detection algorithms,making them more robust and reliable for a wider range of applications, such asnighttime detection. Compared with prior methods, we think different featuresshould be processed specifically, the modality-specific features should beretained and enhanced, while the modality-shared features should becherry-picked from the RGB and thermal IR modalities. Following this idea, anovel and lightweight multispectral feature fusion approach with jointcommon-modality and differential-modality attentions are proposed, namedCross-Modality Attentive Feature Fusion (CMAFF). Given the intermediate featuremaps of RGB and IR images, our module parallel infers attention maps from twoseparate modalities, common- and differential-modality, then the attention mapsare multiplied to the input feature map respectively for adaptive featureenhancement or selection. Extensive experiments demonstrate that our proposedapproach can achieve the state-of-the-art performance at a low computationcost.

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