SynthVLM: High-Efficiency and High-Quality Synthetic Data for Vision Language Models

• URL: http://arxiv.org/abs/2407.20756v3
• Date: Sat, 10 Aug 2024 15:06:12 GMT
• Title: SynthVLM: High-Efficiency and High-Quality Synthetic Data for Vision Language Models
• Authors: Zheng Liu, Hao Liang, Xijie Huang, Wentao Xiong, Qinhan Yu, Linzhuang Sun, Chong Chen, Conghui He, Bin Cui, Wentao Zhang,
• Abstract summary: We introduce SynthVLM, a novel data synthesis pipeline for Vision Large Language Models (VLLMs) Unlike existing methods that generate captions from images, SynthVLM employs advanced diffusion models and high-quality captions to automatically generate and select high-resolution images from captions. We achieve state-of-the-art (SoTA) performance on various vision question answering tasks, maintaining high alignment quality and preserving advanced language abilities.
• Score: 39.55942000935765
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recently, with the rise of web images, managing and understanding large-scale image datasets has become increasingly important. Vision Large Language Models (VLLMs) have recently emerged due to their robust vision-understanding capabilities. However, training these models requires vast amounts of data, posing challenges to efficiency, effectiveness, data quality, and privacy. In this paper, we introduce SynthVLM, a novel data synthesis pipeline for VLLMs. Unlike existing methods that generate captions from images, SynthVLM employs advanced diffusion models and high-quality captions to automatically generate and select high-resolution images from captions, creating precisely aligned image-text pairs. Leveraging these pairs, we achieve state-of-the-art (SoTA) performance on various vision question answering tasks, maintaining high alignment quality and preserving advanced language abilities. Moreover, SynthVLM surpasses traditional GPT-4 Vision-based caption generation methods in performance while significantly reducing computational overhead. Crucially, our method's reliance on purely generated data ensures the preservation of privacy, achieving SoTA performance with just 100k data points (only 18% of the official dataset size).

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