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.

本文提出了一种自适应融合方案，通过两个卷积神经网络中提取的特征和预测的显著性地图，学习一个切换显著性地图的开关来自适应地融合 RGB 和深度模态中所生成的显著性预测。为了实现全监督，利用显著性监督、开关映射监督和边缘保留约束来设计了一个损失函数，并通过端到端训练方式训练整个网络。经过自适应融合策略和边缘保留约束的好处，我们的方法在三个公开数据集上优于已有方法。