Context Refinement with Multi-Attention Fusion for Saliency Segmentation Using Depth-Aware RGBD Sensing

Salient object detection in RGB-D imagery remains challenging due to inconsistent depth quality and suboptimal cross-modal fusion strategies. This paper presents a novel dual-stream architecture that integrates contextual feature refinement with adaptive attention mechanisms for robust RGB-D saliency detection. We extract two features from the ResNet-50 backbone for both the RGB and depth streams, capturing low-level spatial details and high-level semantic representations. We introduce a Contextual Feature Refinement Module (CFRM) that captures multi-scale dependencies through parallel dilated convolutions, enabling hierarchical context aggregation without substantial computational overhead. To enhance discriminative feature learning, we employ channel attention for inter-channel recalibration and a modified spatial attention mechanism utilizing quadruple feature statistics for precise localization. Recognizing that existing depth maps in benchmark datasets are outdated and degraded in quality, we introduce refined depth maps generated with Depth AnythingV2, which significantly improve cross-modal alignment and detection performance. The progressive fusion strategy integrates complementary RGB and depth information across semantic hierarchies, while the saliency prediction block generates high-resolution predictions via gradual spatial expansion. Extensive experiments across six benchmark datasets validate our approach, achieving competitive performance with recent state-of-the-art methods.