Summary of Revisit Large-scale Image-caption Data in Pre-training Multimodal Foundation Models, by Zhengfeng Lai et al.
Revisit Large-Scale Image-Caption Data in Pre-training Multimodal Foundation Models
by Zhengfeng Lai, Vasileios Saveris, Chen Chen, Hong-You Chen, Haotian Zhang, Bowen Zhang, Juan Lao Tebar, Wenze Hu, Zhe Gan, Peter Grasch, Meng Cao, Yinfei Yang
First submitted to arxiv on: 3 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 A novel approach to multimodal foundation model training is proposed in this paper, which explores the role of rewritten captions in improving performance. The study highlights challenges in fully replacing AltTexts with synthetic captions and identifies unique preferences for specific caption formats among different models such as CLIP, multimodal LLMs, and diffusion models. A controllable and scalable captioning pipeline is designed to generate diverse caption formats tailored to various models, with case studies examining Short Synthetic Captions (SSC) towards Dense Synthetic Captions (DSC+). The findings reveal that a hybrid approach combining synthetic captions and AltTexts can outperform using synthetic captions alone, improving alignment and performance. This comprehensive analysis provides valuable insights into optimizing captioning strategies for multimodal foundation model pre-training. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how to make computer models better at understanding pictures by giving them rewritten captions. Right now, there are some problems with making these captions because different models like CLIP or language models have their own preferences. The researchers came up with a new way to create captions that can be adjusted and expanded quickly. They tested this method on several models and found that using both original captions from the internet and rewritten captions together works better than just using one or the other. |
Keywords
» Artificial intelligence » Alignment » Diffusion
