Depth guided feature selection for RGBD salient object detection

Depth information can greatly benefit the saliency detection in RGBD images if they are utilized well. Prevalent methods generally directly fuse depth and RGB features in networks. However, due to the inherent inconsistent between RGB and depth information, the RGB features are easy to be interfered by the intrinsic noise existed in depth features, making the precise RGBD saliency detection still a challenge. In this paper, we propose a novel Depth Guided Feature Selection network (DGFSnet) that takes depth information as prior and dynamically selects the complementary RGB information for RGBD salient object detection. Specifically, DGFSnet first includes a Depth Weight Generation module (DWG) to learn a set of layer-specific weights from multi-scale depth features. Guiding by these learned weights, DGFSnet further devises a Weight-guided Feature Aggregation module (WFA) to assign them to their corresponding RGB layers for dynamically enhancing and selecting saliency-related RGB features. With two modules, DGFSnet is able to effectively integrate the multi-modality complementaries and further highlight salient regions. Experimental results over seven popular RGBD salient object detection benchmarks demonstrate that DGFSnet fairly locates salient regions and effectively segments the complete object.