Uncertainty Guided Refinement for Fine-Grained Salient Object Detection

• URL: http://arxiv.org/abs/2504.09666v1
• Date: Sun, 13 Apr 2025 17:34:37 GMT
• Title: Uncertainty Guided Refinement for Fine-Grained Salient Object Detection
• Authors: Yao Yuan, Pan Gao, Qun Dai, Jie Qin, Wei Xiang,
• Abstract summary: We introduce the uncertainty guidance learning approach to salient object detection (SOD)<n>We design a novel Uncertainty Guided Refinement Attention Network (UGRAN)<n> Experiments on seven benchmark datasets demonstrate the superiority of the proposed UGRAN over the state-of-the-art methodologies.
• Score: 35.6505339733396
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, salient object detection (SOD) methods have achieved impressive performance. However, salient regions predicted by existing methods usually contain unsaturated regions and shadows, which limits the model for reliable fine-grained predictions. To address this, we introduce the uncertainty guidance learning approach to SOD, intended to enhance the model's perception of uncertain regions. Specifically, we design a novel Uncertainty Guided Refinement Attention Network (UGRAN), which incorporates three important components, i.e., the Multilevel Interaction Attention (MIA) module, the Scale Spatial-Consistent Attention (SSCA) module, and the Uncertainty Refinement Attention (URA) module. Unlike conventional methods dedicated to enhancing features, the proposed MIA facilitates the interaction and perception of multilevel features, leveraging the complementary characteristics among multilevel features. Then, through the proposed SSCA, the salient information across diverse scales within the aggregated features can be integrated more comprehensively and integrally. In the subsequent steps, we utilize the uncertainty map generated from the saliency prediction map to enhance the model's perception capability of uncertain regions, generating a highly-saturated fine-grained saliency prediction map. Additionally, we devise an adaptive dynamic partition (ADP) mechanism to minimize the computational overhead of the URA module and improve the utilization of uncertainty guidance. Experiments on seven benchmark datasets demonstrate the superiority of the proposed UGRAN over the state-of-the-art methodologies. Codes will be released at https://github.com/I2-Multimedia-Lab/UGRAN.

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