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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2410.07194 |
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| _version_ | 1866913539904503808 |
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| author | Chen, Shengfu Liu, Hailong Wei, Wenzhao |
| author_facet | Chen, Shengfu Liu, Hailong Wei, Wenzhao |
| contents | This report presents the approach adopted in the Modelscope-Sora challenge, which focuses on fine-tuning data for video generation models. The challenge evaluates participants' ability to analyze, clean, and generate high-quality datasets for video-based text-to-video tasks under specific computational constraints. The provided methodology involves data processing techniques such as video description generation, filtering, and acceleration. This report outlines the procedures and tools utilized to enhance the quality of training data, ensuring improved performance in text-to-video generation models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_07194 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Technical Report: Competition Solution For Modelscope-Sora Chen, Shengfu Liu, Hailong Wei, Wenzhao Computer Vision and Pattern Recognition Artificial Intelligence This report presents the approach adopted in the Modelscope-Sora challenge, which focuses on fine-tuning data for video generation models. The challenge evaluates participants' ability to analyze, clean, and generate high-quality datasets for video-based text-to-video tasks under specific computational constraints. The provided methodology involves data processing techniques such as video description generation, filtering, and acceleration. This report outlines the procedures and tools utilized to enhance the quality of training data, ensuring improved performance in text-to-video generation models. |
| title | Technical Report: Competition Solution For Modelscope-Sora |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2410.07194 |