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IRDFusion: Iterative relation-map difference guided feature fusion for multispectral object detection

delete2026-01-29
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PRE
AI
沈继锋 cover
沈继锋 (Jifeng Shen)
H
Haibo Zhan
X
Xin Zuo
H
Heng Fan
袁晓辉 cover
袁晓辉 (Xiaohui Yuan)
J
Jun Li
W
Wankou Yang
DOI:10.1016/j.patcog.2026.113189delete
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Abstract

Abstract

En 中文
• A Mutual Feature Refinement Module (MFRM) is proposed to enhance modalspecific features of object candidates between two modalities, ensuring robust feature alignment. • Inspired by the feedback differential amplifier circuits, a Differential Feature Feedback Module (DFFM) is proposed to calculate complementary discriminative features between the two modalities and simultaneously filters redundant information. • The MFRM and DFFM are jointly optimized to effectively integrate discriminative complementary information from different modalities through a dynamic differential relationship map feedback mechanism, which provides a new strategy for progressive multispectral feature fusion. • The proposed method IRDFusion, building on MFRM and DFFM, achieves state-of-the-art performance on the FLIR, LLVIP and M3FD dataset.

Journal

Pattern Recognition cover
Pattern Recognition
IF:
7.6
Papers:
1.3W
Citations:
4.5W

Organization

J
Jiangsu University
Scholars:
4.0W
Papers: 2.8W
Citations: 5.5W
N
nanjing normal university
Scholars:
4.0K
Papers: 1.4K
Citations: 0
U
University of North Texas
Scholars:
833
Papers: 466
Citations: 1.1W
S
Southeast University
Scholars:
2.1W
Papers: 8.6K
Citations: 480
J
Jiangsu University of Science and Technology
Scholars:
6.5K
Papers: 2.2K
Citations: 263
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Cited Papers

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