Scan-path Prediction on 360 Degree Images using Saliency Volumes
Marc Assens, Kevin M. McGuinness, Xavier Giró-i-Nieto, Noel Edward O'Connor · arXiv (Cornell University) · 2017
We introduce SaltiNet, a deep neural network for scanpath prediction trained on 360-degree images. The model is based on a temporal-aware novel representation of saliency information named the saliency volume. The first part of the network consists of a model trained to generate saliency volumes, whose parameters are fit by back-propagation computed from a binary cross entropy (BCE) loss over downsampled versions of the saliency volumes. Sampling strategies over these volumes are used to generate scanpaths over the 360-degree images. Our experiments show the advantages of using saliency volumes, and how they can be used for related tasks. Our source code and trained models available at this https URL.