A Review on Deep Learning Techniques for Video Prediction Article Swipe
Sergiu Oprea
,
Pablo Martínez-González
,
Alberto García-García
,
John Alejandro Castro-Vargas
,
Sergio Orts‐Escolano
,
José García‐Rodríguez
,
Antonis Argyros
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1109/tpami.2020.3045007
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1109/tpami.2020.3045007
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 first 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.
Related Topics
Concepts
Computer science
Artificial intelligence
Deep learning
Machine learning
Field (mathematics)
Task (project management)
Key (lock)
Open research
Representation (politics)
Component (thermodynamics)
Data science
Pure mathematics
Management
Thermodynamics
Physics
Politics
Computer security
World Wide Web
Economics
Political science
Law
Mathematics
Metadata
- Type
- review
- Language
- en
- Landing Page
- https://doi.org/10.1109/tpami.2020.3045007
- OA Status
- green
- Cited By
- 278
- References
- 414
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3015879326
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https://openalex.org/W3015879326Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/tpami.2020.3045007Digital Object Identifier
- Title
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A Review on Deep Learning Techniques for Video PredictionWork title
- Type
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reviewOpenAlex work type
- Language
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enPrimary language
- Publication year
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2020Year of publication
- Publication date
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2020-12-15Full publication date if available
- Authors
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Sergiu Oprea, Pablo Martínez-González, Alberto García-García, John Alejandro Castro-Vargas, Sergio Orts‐Escolano, José García‐Rodríguez, Antonis ArgyrosList of authors in order
- Landing page
-
https://doi.org/10.1109/tpami.2020.3045007Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2004.05214Direct OA link when available
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Computer science, Artificial intelligence, Deep learning, Machine learning, Field (mathematics), Task (project management), Key (lock), Open research, Representation (politics), Component (thermodynamics), Data science, Pure mathematics, Management, Thermodynamics, Physics, Politics, Computer security, World Wide Web, Economics, Political science, Law, MathematicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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278Total citation count in OpenAlex
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2025: 51, 2024: 68, 2023: 54, 2022: 68, 2021: 31Per-year citation counts (last 5 years)
- References (count)
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414Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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