Deep video representation learning: a survey
Fuente:
arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866917662782652416 |
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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 |