BLIP3-KALE: Knowledge Augmented Large-Scale Dense Captions
Authors: name: Anas Awadalla, name: Le Xue, name: Manli Shu, name: An Yan, name: Jun Wang, name: Senthil Purushwalkam, name: Sheng Shen, name: Hannah Lee, name: Oscar Lo, name: Jae Sung Park, name: Etash Guha, name: Silvio Savarese, name: Ludwig Schmidt, name: Yejin Choi, name: Caiming Xiong, name: Ran Xu

We introduce BLIP3-KALE, a dataset of 218 million image-text pairs that
bridges the gap between descriptive synthetic captions and factual web-scale
alt-text. KALE augments synthetic dense image captions with web-scale alt-text
to generate factually grounded image captions. Our two-stage approach leverages
large vision-language models and language models to create knowledge-augmented
captions, which are then used to train a specialized VLM for scaling up the
dataset. We train vision-language models on KALE and demonstrate improvements
on vision-language tasks. Our experiments show the utility of KALE for training
more capable and knowledgeable multimodal models. We release the KALE dataset
at https://huggingface.co/datasets/Salesforce/blip3-kale