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Understanding and predicting the human visual attentional mechanism is anactive area of research in the fields of neuroscience and computer vision. Inthis work, we propose DeepFix, a first-of-its-kind fully convolutional neuralnetwork for accurate saliency prediction. Unlike classical works whichcharacterize the saliency map using various hand-crafted features, our modelautomatically learns features in a hierarchical fashion and predicts saliencymap in an end-to-end manner. DeepFix is designed to capture semantics atmultiple scales while taking global context into account using network layerswith very large receptive fields. Generally, fully convolutional nets arespatially invariant which prevents them from modeling location dependentpatterns (e.g. centre-bias). Our network overcomes this limitation byincorporating a novel Location Biased Convolutional layer. We evaluate ourmodel on two challenging eye fixation datasets -- MIT300, CAT2000 and show thatit outperforms other recent approaches by a significant margin.

DeepFix: A Fully Convolutional Neural Network for predicting Human Eye Fixations