Assens M, McGuinness K, Giró-i-Nieto X, O'Connor N. SaltiNet: Scan-path Prediction on 360 Degree Images using Saliency Volumes. In ICCV Workshop on Egocentric Perception, Interaction and Computing. Venice, Italy: IEEE; 2017.  (2.34 MB)


We introduce SaltiNet, a deep neural network for scanpath prediction trained on 360-degree images. The first part of the network consists of a model trained to generate saliency volumes, whose parameters are learned 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.

Winner of three awards at the Salient 360 Challenge at IEEE ICME 2017 (Hong Kong): Best Scan Path, Best Student Scan-path and Audience Award.