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.... More >

RGB–thermal (RGB-T) salient object detection exploits complementary cues from visible and thermal sensors to maintain reliable performance in adverse environments. However, many existing methods (i) fuse modalities before sufficiently enhancing intra-modal semantics and (ii) are sensitive to modality discrepancies caused by heterogeneous sensor characteristics. To address these issues, we propose PACNet (Pyramid Attention Collaboration Network), a hierarchical RGB-T framework that jointly models multi-scale and global context and performs refinement-before-fusion with cross-modal collaboration. Specifically, Dense Atrous Spatial Pyramid Pooling (DASPP) captures multi-scale contextual cues... More >