RGB-D salient object detection via deep fusion of semantics and details

In this paper, we address RGB-D salient object detection task by jointly leveraging semantics and contour details of salient objects. We propose a novel semantics-and-details complementary fusion network to adaptively integrate cross-model and multilevel features. Specifically, we employ two kinds of fusion modules in our model, which are designed for fusing high-level semantic features and integrating contour detail features of the scene components, respectively. The semantics fusion module aggregates high-level interdependent semantic relationships by a nonlinear weighted summation of small and medium receptive fields. Meanwhile, the details module integrates multi-level contour detail features to leverage expressive details of salient objects. We achieve new state-of-the-art salient object detection results on seven RGB-D datasets, that is, STERE, NJU2000, LFSD, NLPR, SSD, DES, and SIP2019 dataset. Experimental results demonstrate that our method outperforms eleven state-of-the-art salient object detection methods.