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SaltiNet: scan-path prediction on 360 degree images using saliency volumes

Assens, Marc, McGuinness, Kevin orcid logoORCID: 0000-0003-1336-6477, Giró-i-Nieto, Xavier orcid logoORCID: 0000-0002-9935-5332 and O'Connor, Noel E. orcid logoORCID: 0000-0002-4033-9135 (2017) SaltiNet: scan-path prediction on 360 degree images using saliency volumes. In: 2nd ICCV EPIC Workshop, 29 Oct 2017, Venice, Italy.

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
Item Type:Conference or Workshop Item (Paper)
Event Type:Workshop
Subjects:Computer Science > Machine learning
Computer Science > Artificial intelligence
Computer Science > Image processing
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Electronic Engineering
Research Institutes and Centres > INSIGHT Centre for Data Analytics
Copyright Information:© 2017 The Authors
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
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

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