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

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Open access paper from Computer Vision Foundation (522,0Kb)
 
10.1109/ICCVW.2017.275
 
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Cita com:
hdl:2117/114891

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Assens, Marc
Giró Nieto, XavierMés informacióMés informació
McGuinness, Kevin
O'Connor, Noel
Document typeConference lecture
Defense date2018
PublisherIEEE Press
Rights accessOpen Access
All rights reserved. This work is protected by the corresponding intellectual and industrial property rights. Without prejudice to any existing legal exemptions, reproduction, distribution, public communication or transformation of this work are prohibited without permission of the copyright holder
ProjectPROCESADO DE INFORMACION HETEROGENEA Y SEÑALES EN GRAFOS PARA BIG DATA. APLICACION EN CRIBADO DE ALTO RENDIMIENTO, TELEDETECCION, MULTIMEDIA Y HCI. (MINECO-TEC2013-43935-R)
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. 
URIhttp://hdl.handle.net/2117/114891
DOI10.1109/ICCVW.2017.275
ISBN978-1-5386-1034-3
Publisher versionhttp://ieeexplore.ieee.org/document/8265485/
Other identifiershttps://arxiv.org/abs/1707.03123
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