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

Cita com:
hdl:2117/114891
Document typeConference lecture
Defense date2018
PublisherIEEE Press
Rights accessOpen Access
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Abstract
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.
CitationAssens, M., Giro, X., McGuinness, K., O'Connor, N. SaltiNet: scan-path prediction on 360 degree images using saliency volumes. A: International Workshop on Egocentric Perception, Interaction and Computing. "2017 IEEE International Conference on Computer Vision Workshops (ICCVW)". IEEE Press, 23/01/2018, p. 2331-2338.
ISBN978-1-5386-1034-3
Publisher versionhttp://ieeexplore.ieee.org/document/8265485/
Other identifiershttps://arxiv.org/abs/1707.03123
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