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
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Funders:
Science Foundation Ireland Grant No. 15/SIRG/3283., TEC2013-43935-R and TEC2016-75976-R, funded by the Spanish Government & European Regional Development Fund (ERDF)., Catalan Government (Generalitat de Catalunya) through AGAUR
ID Code:
21953
Deposited On:
27 Oct 2017 10:42 by
Kevin Mcguinness
. Last Modified 25 Jan 2019 09:54