Cross-Modality Attentive Feature Fusion for Object Detection in Multispectral Remote Sensing Imagery
This work addresses robust object detection for applications like nighttime monitoring in remote sensing, representing an incremental improvement over prior methods.
The paper tackled object detection in multispectral remote sensing imagery by proposing a cross-modality attentive feature fusion method to enhance modality-specific and shared features, achieving state-of-the-art performance with low computation cost.
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.