https://imatge.upc.edu/web/publications/saltinet-scan-path-prediction-360-degree-images-using-saliency-volumes
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.
Awarded at the Salient360! IEEE ICME Grand Challenge 2017 for Best Scan-Path Prediction and Best Scan-Path Prediction Student Prize