SaltiNet: Scan-path Prediction on 360 Degree Images using Saliency Volumes

Autor:	Assens, Marc, McGuinness, Kevin, Giro-i-Nieto, Xavier, O'Connor, Noel E.
Rok vydání:	2017
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Multimedia
Druh dokumentu:	Working Paper
Popis:	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. Comment: Winner of the Best Scan-path Award at the Salient360!: Visual attention modeling for 360 degrees Images Grand Challenge of ICME 2017. Presented at the ICCV 2017 Workshop on Egocentric Perception, Interaction and Computing (EPIC)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1707.03123 Zobrazit plný text záznamu View this record from Arxiv