Circular Complement Network for RGB-D Salient Object Detection

Abstract

With the supplement of texture and geometry cues in depth maps, some difficult scenes of salient object detection (SOD) in 2D images can be overcome. However, some distractors in the depth maps with relatively poor quality may interfere with SOD. Thus, how to suppress the interference of depth maps and extract valuable depth cues, is a critical issue to serve as effective complements to RGB cues. Aiming at addressing this issue, we propose a predict-refine scheme based Circular Complement Network (CCNet), which consists of a prediction subnetwork and a refinement subnetwork. On one hand, since RGB images generally contain more essential information for SOD, we propose a strategy which employs higher-level RGB feature maps to suppress the interference of depth feature maps. With this strategy, a novel Circular Feature Complement (CFC) module is specifically designed to enhance depth feature maps as well as to promote mutual complementarity between RGB feature maps and depth feature maps. The CFC modules are embedded into two subnetworks to achieve the cross-modal interactions at three levels. On the other hand, for the sake of the integration of two subnetworks, a Transmission Bridge (TB) module is proposed to effectively transfer the feature maps of the prediction subnetwork to the refinement subnetwork. The non-salient regions are thus further suppressed in the TB module. Comprehensive experiments on six benchmark datasets show that the proposed CCNet outperforms 13 state-of-the-art models.