Summary of Blip3-kale: Knowledge Augmented Large-scale Dense Captions, by Anas Awadalla et al.
BLIP3-KALE: Knowledge Augmented Large-Scale Dense Captions
by Anas Awadalla, Le Xue, Manli Shu, An Yan, Jun Wang, Senthil Purushwalkam, Sheng Shen, Hannah Lee, Oscar Lo, Jae Sung Park, Etash Guha, Silvio Savarese, Ludwig Schmidt, Yejin Choi, Caiming Xiong, Ran Xu
First submitted to arxiv on: 12 Nov 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 novel dataset, BLIP3-KALE, is introduced to bridge the gap between descriptive synthetic captions and factual web-scale alt-text. The KALE dataset combines large vision-language models with language models to generate factually grounded image captions. This two-stage approach uses knowledge-augmented captions to train a specialized VLM for scaling up the dataset. Vision-language models trained on KALE demonstrate improvements on various tasks, showcasing its utility for training more capable multimodal models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to describe what’s in an image, but you need help remembering what things are called. That’s where BLIP3-KALE comes in – it’s a huge collection of image and text pairs that helps machines understand what’s in pictures. This dataset is special because it uses both made-up captions (synthetic) and real-world descriptions (web-scale alt-text). By combining these, the KALE dataset can help train even better machine learning models to understand images. |
Keywords
» Artificial intelligence » Machine learning
