Summary of Technical Report: Competition Solution For Modelscope-sora, by Shengfu Chen and Hailong Liu and Wenzhao Wei
Technical Report: Competition Solution For Modelscope-Sora
by Shengfu Chen, Hailong Liu, Wenzhao Wei
First submitted to arxiv on: 24 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents the Modelscope-Sora challenge, a competition that evaluates the ability to fine-tune data for video generation models. The challenge focuses on generating high-quality datasets for text-to-video tasks under specific computational constraints. To achieve this, participants employ data processing techniques such as video description generation, filtering, and acceleration. The report outlines the procedures and tools used to enhance the quality of training data, ultimately improving performance in text-to-video generation models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a challenge that helps make video generation models better by giving them high-quality training data. The goal is to see who can clean and prepare datasets for these models to use. The challenge uses special techniques like generating descriptions of videos, filtering out bad data, and speeding up the process. This report explains how they did it and why it matters. |
