tSPM-Net: A probabilistic spatio-temporal approach for scanpath prediction
Martín, Daniel (Universidad de Zaragoza) ; Gutiérrez, Diego (Universidad de Zaragoza) ; Masia, Belén (Universidad de Zaragoza)
Resumen: Predicting the path followed by the viewer’s eyes when observing an image (a scanpath) is a challenging problem, particularly due to the inter- and intra-observer variability and the spatio-temporal dependencies of the visual attention process. Most existing approaches have focused on progressively optimizing the prediction of a gaze point given the previous ones. In this work we propose instead a probabilistic approach, which we call tSPM-Net. We build our method to account for observers’ variability by resorting to Bayesian deep learning and a probabilistic approach. Besides, we optimize our model to jointly consider both spatial and temporal dimensions of scanpaths using a novel spatio-temporal loss function based on a combination of Kullback–Leibler divergence and dynamic time warping. Our tSPM-Net yields results that outperform those of current state-of-the-art approaches, and are closer to the human baseline, suggesting that our model is able to generate scanpaths whose behavior closely resembles those of the real ones.
Idioma: Inglés
DOI: 10.1016/j.cag.2024.103983
Año: 2024
Publicado en: COMPUTERS & GRAPHICS-UK 122 (2024), 103983 [9 pp.]
ISSN: 0097-8493
Factor impacto JCR: 2.8 (2024)
Categ. JCR: COMPUTER SCIENCE, SOFTWARE ENGINEERING rank: 53 / 129 = 0.411 (2024) - Q2 - T2
Factor impacto CITESCORE: 6.1 - Computer Graphics and Computer-Aided Design (Q1) - Computer Vision and Pattern Recognition (Q1) - Engineering (all) (Q1) - Signal Processing (Q1) - Human-Computer Interaction (Q2) - Software (Q2)
Factor impacto SCIMAGO: 0.569 - Computer Graphics and Computer-Aided Design (Q2) - Computer Vision and Pattern Recognition (Q2) - Software (Q2) - Engineering (miscellaneous) (Q2) - Signal Processing (Q2) - Human-Computer Interaction (Q3)
Financiación: info:eu-repo/grantAgreement/ES/AEI/PID2022-141539NB-I00
Financiación: info:eu-repo/grantAgreement/ES/DGA/T34-20R
Financiación: info:eu-repo/grantAgreement/EC/H2020/956585/EU/Predictive Rendering In Manufacture and Engineering/PRIME
Tipo y forma: Article (Published version)
Área (Departamento): Área Lenguajes y Sistemas Inf. (Dpto. Informát.Ingenie.Sistms.)
You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. You may not use the material for commercial purposes.
Exportado de SIDERAL (2026-02-17-20:38:09)
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Idioma: Inglés
DOI: 10.1016/j.cag.2024.103983
Año: 2024
Publicado en: COMPUTERS & GRAPHICS-UK 122 (2024), 103983 [9 pp.]
ISSN: 0097-8493
Factor impacto JCR: 2.8 (2024)
Categ. JCR: COMPUTER SCIENCE, SOFTWARE ENGINEERING rank: 53 / 129 = 0.411 (2024) - Q2 - T2
Factor impacto CITESCORE: 6.1 - Computer Graphics and Computer-Aided Design (Q1) - Computer Vision and Pattern Recognition (Q1) - Engineering (all) (Q1) - Signal Processing (Q1) - Human-Computer Interaction (Q2) - Software (Q2)
Factor impacto SCIMAGO: 0.569 - Computer Graphics and Computer-Aided Design (Q2) - Computer Vision and Pattern Recognition (Q2) - Software (Q2) - Engineering (miscellaneous) (Q2) - Signal Processing (Q2) - Human-Computer Interaction (Q3)
Financiación: info:eu-repo/grantAgreement/ES/AEI/PID2022-141539NB-I00
Financiación: info:eu-repo/grantAgreement/ES/DGA/T34-20R
Financiación: info:eu-repo/grantAgreement/EC/H2020/956585/EU/Predictive Rendering In Manufacture and Engineering/PRIME
Tipo y forma: Article (Published version)
Área (Departamento): Área Lenguajes y Sistemas Inf. (Dpto. Informát.Ingenie.Sistms.)
You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. You may not use the material for commercial purposes. Exportado de SIDERAL (2026-02-17-20:38:09)
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Record created 2024-07-19, last modified 2026-02-17