Summary of Sparrow: Data-efficient Video-llm with Text-to-image Augmentation, by Shukang Yin et al.
Sparrow: Data-Efficient Video-LLM with Text-to-Image Augmentation
by Shukang Yin, Chaoyou Fu, Sirui Zhao, Yunhang Shen, Chunjiang Ge, Yan Yang, Zuwei Long, Yuhan Dai, Yongdong Luo, Haoyu Cao, Tong Xu, Xing Sun, Caifeng Shan, Ran He, Enhong Chen
First submitted to arxiv on: 29 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Computation and Language (cs.CL); Machine Learning (cs.LG)
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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 paper proposes a novel approach to training Multimodal Large Language Models (MLLMs) for video understanding tasks, focusing on developing video-LLMs from a data-centric perspective. The authors revisit the effectiveness of scaling with synthetic data and investigate learning efficiency through data scaling. They find that simply scaling up video data samples leads to low learning efficiency due to lack of instruction diversity. To address this issue, they propose a data augmentation method called Sparrow, which synthesizes video-like samples from pure text instruction data. Experimental results show that the proposed method achieves performance comparable to or even superior to baselines trained with many more samples. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how we can improve machines’ ability to understand videos. Right now, these machines are really good at understanding written text, but they struggle when it comes to understanding videos. The researchers found that one reason for this is that the machines aren’t getting enough instruction or diversity in their training data. To fix this problem, they came up with a new way of generating fake video samples using text-based instructions. This allows them to train the machines more efficiently and effectively. |
Keywords
» Artificial intelligence » Data augmentation » Synthetic data
