Graph-Based Uncertainty Modeling and Multimodal Fusion for Salient Object Detection

• URL: http://arxiv.org/abs/2508.20415v1
• Date: Thu, 28 Aug 2025 04:31:48 GMT
• Title: Graph-Based Uncertainty Modeling and Multimodal Fusion for Salient Object Detection
• Authors: Yuqi Xiong, Wuzhen Shi, Yang Wen, Ruhan Liu,
• Abstract summary: We propose a dynamic uncertainty propagation and multimodal collaborative reasoning network (DUP-MCRNet)<n>DUGC is designed to propagate uncertainty between layers through a sparse graph constructed based on spatial semantic distance.<n>MCF uses learnable modality gating weights to weightedly fuse the attention maps of RGB, depth, and edge features.
• Score: 12.743278093269325
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In view of the problems that existing salient object detection (SOD) methods are prone to losing details, blurring edges, and insufficient fusion of single-modal information in complex scenes, this paper proposes a dynamic uncertainty propagation and multimodal collaborative reasoning network (DUP-MCRNet). Firstly, a dynamic uncertainty graph convolution module (DUGC) is designed to propagate uncertainty between layers through a sparse graph constructed based on spatial semantic distance, and combined with channel adaptive interaction, it effectively improves the detection accuracy of small structures and edge regions. Secondly, a multimodal collaborative fusion strategy (MCF) is proposed, which uses learnable modality gating weights to weightedly fuse the attention maps of RGB, depth, and edge features. It can dynamically adjust the importance of each modality according to different scenes, effectively suppress redundant or interfering information, and strengthen the semantic complementarity and consistency between cross-modalities, thereby improving the ability to identify salient regions under occlusion, weak texture or background interference. Finally, the detection performance at the pixel level and region level is optimized through multi-scale BCE and IoU loss, cross-scale consistency constraints, and uncertainty-guided supervision mechanisms. Extensive experiments show that DUP-MCRNet outperforms various SOD methods on most common benchmark datasets, especially in terms of edge clarity and robustness to complex backgrounds. Our code is publicly available at https://github.com/YukiBear426/DUP-MCRNet.

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