Summary of Koala-36m: a Large-scale Video Dataset Improving Consistency Between Fine-grained Conditions and Video Content, by Qiuheng Wang et al.
Koala-36M: A Large-scale Video Dataset Improving Consistency between Fine-grained Conditions and Video Content
by Qiuheng Wang, Yukai Shi, Jiarong Ou, Rui Chen, Ke Lin, Jiahao Wang, Boyuan Jiang, Haotian Yang, Mingwu Zheng, Xin Tao, Fei Yang, Pengfei Wan, Di Zhang
First submitted to arxiv on: 10 Oct 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 A machine learning-based video generation dataset is crucial for the advancement of visual generation technologies. The quality of these datasets determines the performance of video generation models, which are used to generate realistic videos based on text inputs or other video content. To address the limitations of existing datasets, a large-scale, high-quality video dataset called Koala-36M is introduced. This dataset features accurate temporal splitting, detailed captions, and superior video quality. The approach involves improving the consistency between fine-grained conditions and video content by employing linear classifiers to enhance transition detection accuracy and providing structured captions for split videos. Additionally, a Video Training Suitability Score (VTSS) is developed to filter high-quality videos from the original corpus. Experiments demonstrate the effectiveness of this data processing pipeline and the quality of the proposed Koala-36M dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new video generation dataset called Koala-36M has been created to help machines learn how to make realistic videos based on text or other videos. This dataset is important because it can be used to train models that can generate high-quality videos, which have many real-life applications. The dataset is better than others because it has accurate split points in time, detailed captions, and high-quality video content. To create this dataset, the researchers developed a way to improve how well models match text with video content. They also created a score to help them pick out the best videos for training their models. |
Keywords
» Artificial intelligence » Machine learning
