Summary of Video Dataflywheel: Resolving the Impossible Data Trinity in Video-language Understanding, by Xiao Wang et al.
Video DataFlywheel: Resolving the Impossible Data Trinity in Video-Language Understanding
by Xiao Wang, Jianlong Wu, Zijia Lin, Fuzheng Zhang, Di Zhang, Liqiang Nie
First submitted to arxiv on: 29 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Computation and Language (cs.CL); Machine Learning (cs.LG); Multimedia (cs.MM)
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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 The recent success of pre-training in video-language understanding has led to the development of large-scale datasets, but these datasets often suffer from data scarcity issues. This paper reveals an “impossible trinity” between data quantity, diversity, and quality, making it challenging to achieve high-quality annotations. To address this issue, the authors introduce the Video DataFlywheel framework, which iteratively refines video annotations using a video-language model and noise control methods. The framework is composed of two main components: iterative refinement and AdaTaiLr, a novel noise control method that requires weaker assumptions on noise distribution. Experimental results show that the proposed framework outperforms existing data refinement baselines by 3% and improves dataset quality with minimal diversity loss. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The goal of this paper is to make large-scale video datasets better. Right now, these datasets have some big problems: there’s not enough data, it’s not diverse enough, or the data is low-quality. The authors came up with a solution called Video DataFlywheel. This framework takes existing video data and makes it even better by using a special kind of AI model. They also developed a new way to control noise in the data, which helps make sure the data stays good as it gets bigger. In tests, this approach worked really well: it improved the quality of the data without losing any diversity, and it helped machines understand videos even better. |
Keywords
* Artificial intelligence * Language model * Language understanding
