SaltiNet: Scan-Path Prediction on 360 Degree Images Using Saliency Volumes

We introduce SaltiNet, a deep neural network for scan-path 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, w...

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Bibliographic Details
Main Authors: Assens, Marc, Giro-i-Nieto, Xavier, McGuinness, Kevin, O'Connor, Noel E.
Format: Conference Proceeding
Language:English
Subjects:
Biological system modeling
Computational modeling
Observers
Predictive models
Training
Visualization
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Summary:We introduce SaltiNet, a deep neural network for scan-path 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 scan-paths 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.
ISSN:2473-9944
DOI:10.1109/ICCVW.2017.275