Triple-Complementary Network For Rgb-D Salient Object Detection

Most of the existing RGB-D saliency detectors have tried different strategies to fuse RGB and depth information to generate better saliency detection results. However, the ability of only using RGB image to detect the salient object is ignored in RGB-D saliency detectors. In this paper, we propose a triple-complementary network for fully exploring the RGB and depth information. The first two sub-networks are used for extracting saliency from RGB image and RGB-D image pair, respectively. The third sub-network refines the saliency map by comprehensively considering RGB image, depth map and the refined saliency map of the first two sub-networks. We propose depth weighted refinement to suppress the high contrast objects with large depth values. The proposed method not only fully explores the information of RGB but also makes the RGB and depth tightly coupled. The experiments on five RGB-D saliency detection datasets show the superiority of the proposed method over the state-of-the-art RGB-D saliency detectors.