  We propose the first stochastic framework to employ uncertainty for RGB-D
saliency detection by learning from the data labeling process. Existing RGB-D
saliency detection models treat this task as a point estimation problem by
predicting a single saliency map following a deterministic learning pipeline.
We argue that, however, the deterministic solution is relatively ill-posed.
Inspired by the saliency data labeling process, we propose a generative
architecture to achieve probabilistic RGB-D saliency detection which utilizes a
latent variable to model the labeling variations. Our framework includes two
main models: 1) a generator model, which maps the input image and latent
variable to stochastic saliency prediction, and 2) an inference model, which
gradually updates the latent variable by sampling it from the true or
approximate posterior distribution. The generator model is an encoder-decoder
saliency network. To infer the latent variable, we introduce two different
solutions: i) a Conditional Variational Auto-encoder with an extra encoder to
approximate the posterior distribution of the latent variable; and ii) an
Alternating Back-Propagation technique, which directly samples the latent
variable from the true posterior distribution. Qualitative and quantitative
results on six challenging RGB-D benchmark datasets show our approach's
superior performance in learning the distribution of saliency maps. The source
code is publicly available via our project page:
https://github.com/JingZhang617/UCNet.
