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Main Authors: Ravanbakhsh, Elham, Liang, Yongqing, Ramanujam, J., Li, Xin
Format: Preprint
Published: 2024
Subjects:
Online Access:https://arxiv.org/abs/2405.06574
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author Ravanbakhsh, Elham
Liang, Yongqing
Ramanujam, J.
Li, Xin
author_facet Ravanbakhsh, Elham
Liang, Yongqing
Ramanujam, J.
Li, Xin
contents This paper provides a review on representation learning for videos. We classify recent spatiotemporal feature learning methods for sequential visual data and compare their pros and cons for general video analysis. Building effective features for videos is a fundamental problem in computer vision tasks involving video analysis and understanding. Existing features can be generally categorized into spatial and temporal features. Their effectiveness under variations of illumination, occlusion, view and background are discussed. Finally, we discuss the remaining challenges in existing deep video representation learning studies.
format Preprint
id arxiv_https___arxiv_org_abs_2405_06574
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Deep video representation learning: a survey
Ravanbakhsh, Elham
Liang, Yongqing
Ramanujam, J.
Li, Xin
Computer Vision and Pattern Recognition
This paper provides a review on representation learning for videos. We classify recent spatiotemporal feature learning methods for sequential visual data and compare their pros and cons for general video analysis. Building effective features for videos is a fundamental problem in computer vision tasks involving video analysis and understanding. Existing features can be generally categorized into spatial and temporal features. Their effectiveness under variations of illumination, occlusion, view and background are discussed. Finally, we discuss the remaining challenges in existing deep video representation learning studies.
title Deep video representation learning: a survey
topic Computer Vision and Pattern Recognition
url https://arxiv.org/abs/2405.06574