162446 20251017144635.0 doi 10.1016/j.cag.2022.06.002 sideral 129122 ART-2022-129122 eng Bernal Berdun, Edurne Universidad de Zaragoza (orcid)0000-0002-5275-8652 SST-Sal: A spherical spatio-temporal approach for saliency prediction in 360 videos 2022 Access copy available to the general public Unrestricted Virtual reality (VR) has the potential to change the way people consume content, and has been predicted to become the next big computing paradigm. However, much remains unknown about the grammar and visual language of this new medium, and understanding and predicting how humans behave in virtual environments remains an open problem. In this work, we propose a novel saliency prediction model which exploits the joint potential of spherical convolutions and recurrent neural networks to extract and model the inherent spatio-temporal features from 360° videos. We employ Convolutional Long Short-Term Memory cells (ConvLSTMs) to account for temporal information at the time of feature extraction rather than to post-process spatial features as in previous works. To facilitate spatio-temporal learning, we provide the network with an estimation of the optical flow between 360° frames, since motion is known to be a highly salient feature in dynamic content. Our model is trained with a novel spherical Kullback–Leibler Divergence (KLDiv) loss function specifically tailored for saliency prediction in 360° content. Our approach outperforms previous state-of-the-art works, being able to mimic human visual attention when exploring dynamic 360° videos. info:eu-repo/grantAgreement/ES/AEI/PID2019-105004GB-I00 info:eu-repo/grantAgreement/EC/H2020/682080/EU/Intuitive editing of visual appearance from real-world datasets/CHAMELEON This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 682080-CHAMELEON info:eu-repo/grantAgreement/EC/H2020/956585/EU/Predictive Rendering In Manufacture and Engineering/PRIME This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 956585-PRIME info:eu-repo/semantics/openAccess by-nc-nd https://creativecommons.org/licenses/by-nc-nd/4.0/deed.es 2.5 2022 COMPUTER SCIENCE, SOFTWARE ENGINEERING 52 / 108 = 0.481 2022 Q2 T2 0.539 2022 Computer Graphics and Computer-Aided Design 2022 Q2 Computer Vision and Pattern Recognition 2022 Q2 Engineering (miscellaneous) 2022 Q2 Signal Processing 2022 Q2 Human-Computer Interaction 2022 Q3 Software 2022 Q3 4.9 2022 info:eu-repo/semantics/article info:eu-repo/semantics/acceptedVersion Martín Serrano, Daniel Universidad de Zaragoza (orcid)0000-0002-0073-6398 Gutiérrez Pérez, Diego Universidad de Zaragoza (orcid)0000-0002-7503-7022 Masiá Corcoy, Belén Universidad de Zaragoza (orcid)0000-0003-0060-7278 5007 570 Universidad de Zaragoza Dpto. Informát.Ingenie.Sistms. Área Lenguajes y Sistemas Inf. 106 (2022), 200-209 Comput. graph. COMPUTERS & GRAPHICS-UK 0097-8493 17972271 http://zaguan.unizar.es/record/162446/files/texto_completo.pdf Postprint 2887919 http://zaguan.unizar.es/record/162446/files/texto_completo.jpg?subformat=icon icon Postprint oai:zaguan.unizar.es:162446 articulos driver 2025-10-17-14:28:27 ARTICLE