  Visual saliency models have recently begun to incorporate deep learning to
achieve predictive capacity much greater than previous unsupervised methods.
However, most existing models predict saliency using local mechanisms limited
to the receptive field of the network. We propose a model that incorporates
global scene semantic information in addition to local information gathered by
a convolutional neural network. Our model is formulated as a mixture of
experts. Each expert network is trained to predict saliency for a set of
closely related images. The final saliency map is computed as a weighted
mixture of the expert networks' output, with weights determined by a separate
gating network. This gating network is guided by global scene information to
predict weights. The expert networks and the gating network are trained
simultaneously in an end-to-end manner. We show that our mixture formulation
leads to improvement in performance over an otherwise identical non-mixture
model that does not incorporate global scene information.
