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- W4200631567 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 thermal 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." @default.
- W4200631567 created "2021-12-31" @default.
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- W4200631567 date "2022-10-01" @default.
- W4200631567 modified "2023-10-09" @default.
- W4200631567 title "Cross-modality attentive feature fusion for object detection in multispectral remote sensing imagery" @default.
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- W4200631567 doi "https://doi.org/10.1016/j.patcog.2022.108786" @default.
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