  RGB-D salient object detection aims to identify the most visually distinctive
objects in a pair of color and depth images. Based upon an observation that
most of the salient objects may stand out at least in one modality, this paper
proposes an adaptive fusion scheme to fuse saliency predictions generated from
two modalities. Specifically, we design a two-streamed convolutional neural
network (CNN), each of which extracts features and predicts a saliency map from
either RGB or depth modality. Then, a saliency fusion module learns a switch
map that is used to adaptively fuse the predicted saliency maps. A loss
function composed of saliency supervision, switch map supervision, and
edge-preserving constraints is designed to make full supervision, and the
entire network is trained in an end-to-end manner. Benefited from the adaptive
fusion strategy and the edge-preserving constraint, our approach outperforms
state-of-the-art methods on three publicly available datasets.
