CFRNet: Cross-Attention-Based Fusion and Refinement Network for Enhanced RGB-T Salient Object Detection

Existing deep learning-based RGB-T salient object detection methods often struggle with effectively fusing RGB and thermal features. Therefore, obtaining high-quality features and fully integrating these two modalities are central research focuses. We developed an illumination prior-based coefficient predictor (MICP) to determine optimal interaction weights. We then designed a saliency-guided encoder (SG Encoder) to extract multi-scale thermal features incorporating saliency information. The SG Encoder guides the extraction of thermal features by leveraging their correlation with RGB features, particularly those with strong semantic relationships to salient object detection tasks. Finally, we employed a Cross-attention-based Fusion and Refinement Module (CrossFRM) to refine the fused features. The robust thermal features help refine the spatial focus of the fused features, aligning them more closely with salient objects. Experimental results demonstrate that our proposed approach can more accurately locate salient objects, significantly improving performance compared to 11 state-of-the-art methods.