Dynamic Weighted Fusion and Progressive Refinement Network for Visible-Depth-Thermal Salient Object Detection

The introduction of depth/thermal modality has significantly enhanced the performance of dual-modal salient object detection (SOD) methods. However, depth maps and thermal images are prone to environmental interference, making them insufficient for providing salient information. To address this challenge, triple-modal SOD methods have been proposed. However, these methods often overlook the detrimental effects of defective modalities during fusion, leading to subpar performance. To tackle this issue, we present a novel dynamic weighted fusion and progressive refinement network (DWFPRNet) for Visible-Depth-Thermal (V-D-T) SOD. Specifically, we first use the dual-modal fusion module (DFM) to fuse dual modalities, thereby obtaining fused features. Subsequently, the modality selective fusion module (MSFM) mines complementary information between fused features, considering both fusion features and the quality of feature maps, to achieve weighted fusion. Finally, we design a progressive refinement decoder (PRD) to realize interaction and multi-scale learning among different scale features and generate high-quality saliency maps. Extensive experiments conducted on the VDT-2048 public dataset demonstrate that our method outperforms existing state-of-the-art multi-modal methods.