DAST: Depth-Aware Assessment and Synthesis Transformer for RGB-D Salient Object Detection

The introduction and popularity of depth maps have brought new vitality and growth into salient object detection (SOD), and plentiful RGB-D SOD methods have been proposed, which mainly focus on how to utilize and integrate the depth map. Although existing methods have achieved promising performance, the negative effects of lowquality depth maps have not been effectively addressed. In this paper, we solve the problem with a strategy of judging low-quality depth maps and assigning low factors to low-quality depth maps. To this end, we proposed a novel Transformer-based SOD framework, namely Depth-aware Assessment and Synthesis Transformer (DAST), to further improve the performance of RGB-D SOD. The proposed DAST involves two primary designs: 1) a Swin Transformer-based encoder is employed instead of a convolutional neural network for more effective feature extraction and long-range dependencies capture; 2) a Depth Assessment and Synthesis (DAS) module is proposed to judge the quality of depth maps and fuse the multi-modality salient features by computing the difference of saliency maps from RGB and depth streams in a coarse-to-fine manner. Extensive experiments on five benchmark datasets demonstrate that the proposed DAST achieves favorable performance as compared with other state-of-the-art (SOTA) methods.