  Understanding and predicting the human visual attentional mechanism is an
active area of research in the fields of neuroscience and computer vision. In
this work, we propose DeepFix, a first-of-its-kind fully convolutional neural
network for accurate saliency prediction. Unlike classical works which
characterize the saliency map using various hand-crafted features, our model
automatically learns features in a hierarchical fashion and predicts saliency
map in an end-to-end manner. DeepFix is designed to capture semantics at
multiple scales while taking global context into account using network layers
with very large receptive fields. Generally, fully convolutional nets are
spatially invariant which prevents them from modeling location dependent
patterns (e.g. centre-bias). Our network overcomes this limitation by
incorporating a novel Location Biased Convolutional layer. We evaluate our
model on two challenging eye fixation datasets -- MIT300, CAT2000 and show that
it outperforms other recent approaches by a significant margin.
