  In this paper, we aim to develop an efficient and compact deep network for
RGB-D salient object detection, where the depth image provides complementary
information to boost performance in complex scenarios. Starting from a coarse
initial prediction by a multi-scale residual block, we propose a progressively
guided alternate refinement network to refine it. Instead of using ImageNet
pre-trained backbone network, we first construct a lightweight depth stream by
learning from scratch, which can extract complementary features more
efficiently with less redundancy. Then, different from the existing fusion
based methods, RGB and depth features are fed into proposed guided residual
(GR) blocks alternately to reduce their mutual degradation. By assigning
progressive guidance in the stacked GR blocks within each side-output, the
false detection and missing parts can be well remedied. Extensive experiments
on seven benchmark datasets demonstrate that our model outperforms existing
state-of-the-art approaches by a large margin, and also shows superiority in
efficiency (71 FPS) and model size (64.9 MB).
