  Recent saliency models extensively explore to incorporate multi-scale
contextual information from Convolutional Neural Networks (CNNs). Besides
direct fusion strategies, many approaches introduce message-passing to enhance
CNN features or predictions. However, the messages are mainly transmitted in
two ways, by feature-to-feature passing, and by prediction-to-prediction
passing. In this paper, we add message-passing between features and predictions
and propose a deep unified CRF saliency model . We design a novel cascade CRFs
architecture with CNN to jointly refine deep features and predictions at each
scale and progressively compute a final refined saliency map. We formulate the
CRF graphical model that involves message-passing of feature-feature,
feature-prediction, and prediction-prediction, from the coarse scale to the
finer scale, to update the features and the corresponding predictions. Also, we
formulate the mean-field updates for joint end-to-end model training with CNN
through back propagation. The proposed deep unified CRF saliency model is
evaluated over six datasets and shows highly competitive performance among the
state of the arts.
