  Benefiting from the spatial cues embedded in depth images, recent progress on
RGB-D saliency detection shows impressive ability on some challenge scenarios.
However, there are still two limitations. One hand is that the pooling and
upsampling operations in FCNs might cause blur object boundaries. On the other
hand, using an additional depth-network to extract depth features might lead to
high computation and storage cost. The reliance on depth inputs during testing
also limits the practical applications of current RGB-D models. In this paper,
we propose a novel collaborative learning framework where edge, depth and
saliency are leveraged in a more efficient way, which solves those problems
tactfully. The explicitly extracted edge information goes together with
saliency to give more emphasis to the salient regions and object boundaries.
Depth and saliency learning is innovatively integrated into the high-level
feature learning process in a mutual-benefit manner. This strategy enables the
network to be free of using extra depth networks and depth inputs to make
inference. To this end, it makes our model more lightweight, faster and more
versatile. Experiment results on seven benchmark datasets show its superior
performance.
