  Salient object detection is a fundamental topic in computer vision. Previous
methods based on RGB-D often suffer from the incompatibility of multi-modal
feature fusion and the insufficiency of multi-scale feature aggregation. To
tackle these two dilemmas, we propose a novel multi-modal and multi-scale
refined network (M2RNet). Three essential components are presented in this
network. The nested dual attention module (NDAM) explicitly exploits the
combined features of RGB and depth flows. The adjacent interactive aggregation
module (AIAM) gradually integrates the neighbor features of high, middle and
low levels. The joint hybrid optimization loss (JHOL) makes the predictions
have a prominent outline. Extensive experiments demonstrate that our method
outperforms other state-of-the-art approaches.
