  Most saliency estimation methods aim to explicitly model low-level
conspicuity cues such as edges or blobs and may additionally incorporate
top-down cues using face or text detection. Data-driven methods for training
saliency models using eye-fixation data are increasingly popular, particularly
with the introduction of large-scale datasets and deep architectures. However,
current methods in this latter paradigm use loss functions designed for
classification or regression tasks whereas saliency estimation is evaluated on
topographical maps. In this work, we introduce a new saliency map model which
formulates a map as a generalized Bernoulli distribution. We then train a deep
architecture to predict such maps using novel loss functions which pair the
softmax activation function with measures designed to compute distances between
probability distributions. We show in extensive experiments the effectiveness
of such loss functions over standard ones on four public benchmark datasets,
and demonstrate improved performance over state-of-the-art saliency methods.
