Hierarchical Decoding Network Based on Swin Transformer for Detecting Salient Objects in RGB-T Images

Although conventional deep convolutional neural networks are effective for contextual semantic segmentation of objects, recent vision transformers can capture global information of an image and are better at capturing semantic associations over longer ranges. In addition, some existing saliency detection methods disregard the guidance of high-level semantic information for low-level features during decoding, and only use layer-by-layer transmission for encoding. Therefore, we propose a hierarchical decoding network based on a swin transformer to perform red-green-blue and thermal (RGB-T) salient object detection (SOD). First, a sine-cosine fusion module performs multimodality intersections and exploits complementarity. As a second fusion stage, an advanced semantic information guidance module adjusts high-level semantic information and low-level detailed characteristics. Finally, a global saliency perception module fuses cross-layer information in a top-down path. Comprehensive experiments demonstrate that the proposed network outperforms 12 state-of-the-art methods on three RGB-T SOD datasets.