Cross-modality attentive feature fusion for object detection in multispectral remote sensing imagery
•We propose a simple yet effective CMAFF module that can fuse the complementary information of multispectral remote sensing images with joint common-modality and differential-modality attentions.•We confirm the effectiveness of our cross-modality fusion attention module through extensive ablation st...
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| Published in | Pattern recognition Vol. 130; p. 108786 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Ltd
01.10.2022
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0031-3203 1873-5142 |
| DOI | 10.1016/j.patcog.2022.108786 |
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| Abstract | •We propose a simple yet effective CMAFF module that can fuse the complementary information of multispectral remote sensing images with joint common-modality and differential-modality attentions.•We confirm the effectiveness of our cross-modality fusion attention module through extensive ablation studies.•We design a new two-stream object detection network YOLOFusion for multispectral remote sensing images and verify its performance.
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. |
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| AbstractList | •We propose a simple yet effective CMAFF module that can fuse the complementary information of multispectral remote sensing images with joint common-modality and differential-modality attentions.•We confirm the effectiveness of our cross-modality fusion attention module through extensive ablation studies.•We design a new two-stream object detection network YOLOFusion for multispectral remote sensing images and verify its performance.
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. |
| ArticleNumber | 108786 |
| Author | Zhaokui, Wang Qingyun, Fang |
| Author_xml | – sequence: 1 givenname: Fang surname: Qingyun fullname: Qingyun, Fang email: fqy17@mails.tsinghua.edu.cn – sequence: 2 givenname: Wang surname: Zhaokui fullname: Zhaokui, Wang email: wangzk@tsinghua.edu.cn |
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| Title | Cross-modality attentive feature fusion for object detection in multispectral remote sensing imagery |
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