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We introduce SaltiNet, a deep neural network for scanpath prediction trainedon 360-degree images. The model is based on a temporal-aware novelrepresentation of saliency information named the saliency volume. The firstpart of the network consists of a model trained to generate saliency volumes,whose parameters are fit by back-propagation computed from a binary crossentropy (BCE) loss over downsampled versions of the saliency volumes. Samplingstrategies over these volumes are used to generate scanpaths over the360-degree images. Our experiments show the advantages of using saliencyvolumes, and how they can be used for related tasks. Our source code andtrained models available athttps://github.com/massens/saliency-360salient-2017.

SaltiNet: Scan-path Prediction on 360 Degree Images using Saliency Volumes