Summary of the Article:
- Large-scale web-crawled datasets are crucial for pre-training vision-language models like CLIP.
- Noise and irrelevance in AltTexts from web-crawled data make achieving accurate image-text alignment challenging.
- Current methods using large language models (LLMs) for caption rewriting have shown potential on smaller curated datasets such as CC3M and CC12M.
- This study proposes a scalable pipeline for rewriting noisy captions, focusing on incorporating visual concepts into captions.
Author’s Take:
In the realm of enhancing vision-language models like CLIP, the significance of high-quality, large-scale datasets cannot be overstated. Overcoming the hurdles of noise and irrelevance in web-crawled data is key to achieving precise image-text alignment, with novel approaches like incorporating visual concepts showing promise for better caption rewriting techniques.