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BLIP3-KALE: Knowledge Augmented Large-Scale Dense Captions

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arxiv 2411.07461 v1 pith:GJZZ22DR submitted 2024-11-12 cs.CV cs.AI

BLIP3-KALE: Knowledge Augmented Large-Scale Dense Captions

Anas Awadalla , Le Xue , Manli Shu , An Yan , Jun Wang , Senthil Purushwalkam , Sheng Shen , Hannah Lee
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Oscar Lo Jae Sung Park Etash Guha Silvio Savarese Ludwig Schmidt Yejin Choi Caiming Xiong Ran Xu
This is my paper
classification cs.CV cs.AI
keywords captionskalemodelsblip3-kaledatasetvision-languagealt-textdense
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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

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