Guided residual network for RGB-D salient object detection with efficient depth feature learning

RGB-D salient object detection aims at identifying the most attractive parts from a RGB image and its corresponding depth image, which has been widely applied in many computer vision tasks. However, there are still two challenges: (1) how to quickly and effectively integrate the cross-modal features from the RGB-D data; and (2) how to mitigate the negative impact from the low-quality depth map. The previous methods mostly employ a two-stream architecture which adopts two backbone network to process RGB-D data and ignore the quality of depth map. In this paper, we propose a guided residual network to address these two issues. On the one hand, we design a simpler and efficient depth branch only using one convolutional layer and three residual modules to extract depth features instead of employing a pre-trained backbone to handle the depth data, and fuse RGB features and depth features in a multi-scale manner for refinement with top-down guidance. On the other hand, we add adaptive weight to depth maps to control the fusion between them, which mitigates the negative influence of unreliable depth map. Experimental results compared with 13 state-of-the-art methods on 7 datasets demonstrate the validity of the proposed approach both quantitatively and qualitatively, especially in efficiency (102 FPS) and compactness (64.2 MB).

Automated summary

This paper proposes a guided residual network to address the challenges in RGB-D salient object detection, with a simplified depth branch and adaptive weight enhancement method, effectively improving detection performance.