  Existing RGB-D saliency detection models do not explicitly encourage RGB and
depth to achieve effective multi-modal learning. In this paper, we introduce a
novel multi-stage cascaded learning framework via mutual information
minimization to "explicitly" model the multi-modal information between RGB
image and depth data. Specifically, we first map the feature of each mode to a
lower dimensional feature vector, and adopt mutual information minimization as
a regularizer to reduce the redundancy between appearance features from RGB and
geometric features from depth. We then perform multi-stage cascaded learning to
impose the mutual information minimization constraint at every stage of the
network. Extensive experiments on benchmark RGB-D saliency datasets illustrate
the effectiveness of our framework. Further, to prosper the development of this
field, we contribute the largest (7x larger than NJU2K) dataset, which contains
15,625 image pairs with high quality
polygon-/scribble-/object-/instance-/rank-level annotations. Based on these
rich labels, we additionally construct four new benchmarks with strong
baselines and observe some interesting phenomena, which can motivate future
model design. Source code and dataset are available at
"https://github.com/JingZhang617/cascaded_rgbd_sod".
