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 https://github.com/massens/saliency-360salient-2017.
@article{arxiv.1707.03123,
title = {SaltiNet: Scan-path Prediction on 360 Degree Images using Saliency Volumes},
author = {Marc Assens and Kevin McGuinness and Xavier Giro-i-Nieto and Noel E. O'Connor},
journal= {arXiv preprint arXiv:1707.03123},
year = {2017}
}
Comments
Winner of the Best Scan-path Award at the Salient360!: Visual attention modeling for 360 degrees Images Grand Challenge of ICME 2017. Presented at the ICCV 2017 Workshop on Egocentric Perception, Interaction and Computing (EPIC)