A Saliency Enhanced Feature Fusion based multiscale RGB-D Salient Object
  Detection Network
Rui Huang Qingyi Zhao Yan Xing Sihua Gao Weifeng Xu Yuxiang Zhang Wei Fan
Multiscale convolutional neural network (CNN) has demonstrated remarkable
capabilities in solving various vision problems. However, fusing features of
different scales alwaysresults in large model sizes, impeding the application
of multiscale CNNs in RGB-D saliency detection. In this paper, we propose a
customized feature fusion module, called Saliency Enhanced Feature Fusion
(SEFF), for RGB-D saliency detection. SEFF utilizes saliency maps of the
neighboring scales to enhance the necessary features for fusing, resulting in
more representative fused features. Our multiscale RGB-D saliency detector uses
SEFF and processes images with three different scales. SEFF is used to fuse the
features of RGB and depth images, as well as the features of decoders at
different scales. Extensive experiments on five benchmark datasets have
demonstrated the superiority of our method over ten SOTA saliency detectors.