A Review on Deep Learning Techniques for Video Prediction
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http://hdl.handle.net/10045/121956
Título: | A Review on Deep Learning Techniques for Video Prediction |
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Autor/es: | Oprea, Sergiu | Martínez González, Pablo | Garcia-Garcia, Alberto | Castro-Vargas, John Alejandro | Orts-Escolano, Sergio | Garcia-Rodriguez, Jose | Argyros, Antonis |
Grupo/s de investigación o GITE: | Robótica y Visión Tridimensional (RoViT) | Arquitecturas Inteligentes Aplicadas (AIA) |
Centro, Departamento o Servicio: | Universidad de Alicante. Departamento de Ciencia de la Computación e Inteligencia Artificial | Universidad de Alicante. Departamento de Tecnología Informática y Computación |
Palabras clave: | Video prediction | Future frame prediction | Deep learning | Representation learning | Self-supervised learning |
Área/s de conocimiento: | Ciencia de la Computación e Inteligencia Artificial | Arquitectura y Tecnología de Computadores |
Fecha de publicación: | 15-dic-2020 |
Editor: | IEEE |
Cita bibliográfica: | IEEE Transactions on Pattern Analysis and Machine Intelligence. 2022, 44(6): 2806-2826. https://doi.org/10.1109/TPAMI.2020.3045007 |
Resumen: | The ability to predict, anticipate and reason about future outcomes is a key component of intelligent decision-making systems. In light of the success of deep learning in computer vision, deep-learning-based video prediction emerged as a promising research direction. Defined as a self-supervised learning task, video prediction represents a suitable framework for representation learning, as it demonstrated potential capabilities for extracting meaningful representations of the underlying patterns in natural videos. Motivated by the increasing interest in this task, we provide a review on the deep learning methods for prediction in video sequences. We firstly define the video prediction fundamentals, as well as mandatory background concepts and the most used datasets. Next, we carefully analyze existing video prediction models organized according to a proposed taxonomy, highlighting their contributions and their significance in the field. The summary of the datasets and methods is accompanied with experimental results that facilitate the assessment of the state of the art on a quantitative basis. The paper is summarized by drawing some general conclusions, identifying open research challenges and by pointing out future research directions. |
Patrocinador/es: | This work has been funded by the Spanish Government PID2019-104818RB-I00 grant for the MoDeaAS project, supported with Feder funds. This work has also been supported by two Spanish national grants for PhD studies, FPU17/00166, and ACIF/2018/197 respectively. |
URI: | http://hdl.handle.net/10045/121956 |
ISSN: | 0162-8828 (Print) | 1939-3539 (Online) |
DOI: | 10.1109/TPAMI.2020.3045007 |
Idioma: | eng |
Tipo: | info:eu-repo/semantics/article |
Derechos: | © 2020 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission |
Revisión científica: | si |
Versión del editor: | https://doi.org/10.1109/TPAMI.2020.3045007 |
Aparece en las colecciones: | INV - RoViT - Artículos de Revistas INV - AIA - Artículos de Revistas |
Archivos en este ítem:
Archivo | Descripción | Tamaño | Formato | |
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Oprea_etal_2020_IEEE-TPAMI_accepted.pdf | Accepted Manuscript (acceso abierto) | 2,28 MB | Adobe PDF | Abrir Vista previa |
Oprea_etal_2020_IEEE-TPAMI_final.pdf | Versión final (acceso restringido) | 2,21 MB | Adobe PDF | Abrir Solicitar una copia |
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