Efficient Depth-Included Residual Refinement Network for RGB-D Saliency Detection.

RGB-D saliency detection aims to segment eye-catching objects from images with the help of depth. Although many excellent methods raised, it is still difficult to locate salient objects accurately and efficiently, which lies in two challenges: (1) It is difficult to seamlessly and efficiently integrate cross-modal features from RGB-D inputs; (2) Low-quality depth maps have a serious negative impact on the final prediction results. The existing methods use two backbone networks to extract saliency features, which also introduce much redundancy. To address issues, we propose a simple and efficient deep feature refinement module to extract complementary depth features. We also design a depth correction module to filter out noisy depth input adaptively. Experiments with 13 recently proposed methods on 7 datasets demonstrate the effectiveness of the proposed approach both quantitatively and qualitatively, especially in efficiency and compactness.