POINTS: Improving Your Vision-language Model with Affordable Strategies

• URL: http://arxiv.org/abs/2409.04828v3
• Date: Tue, 5 Nov 2024 02:32:06 GMT
• Title: POINTS: Improving Your Vision-language Model with Affordable Strategies
• Authors: Yuan Liu, Zhongyin Zhao, Ziyuan Zhuang, Le Tian, Xiao Zhou, Jie Zhou,
• Abstract summary: We train a robust baseline model using latest advancements in vision-language models. We filter pre-training data using perplexity, selecting the lowest perplexity data for training. During visual instruction tuning, we used model soup on different datasets when adding more datasets yielded marginal improvements.
• Score: 28.611705477757454
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In recent years, vision-language models have made significant strides, excelling in tasks like optical character recognition and geometric problem-solving. However, several critical issues remain: 1) Proprietary models often lack transparency about their architectures, while open-source models need more detailed ablations of their training strategies. 2) Pre-training data in open-source works is under-explored, with datasets added empirically, making the process cumbersome. 3) Fine-tuning often focuses on adding datasets, leading to diminishing returns. To address these issues, we propose the following contributions: 1) We trained a robust baseline model using the latest advancements in vision-language models, introducing effective improvements and conducting comprehensive ablation and validation for each technique. 2) Inspired by recent work on large language models, we filtered pre-training data using perplexity, selecting the lowest perplexity data for training. This approach allowed us to train on a curated 1M dataset, achieving competitive performance. 3) During visual instruction tuning, we used model soup on different datasets when adding more datasets yielded marginal improvements. These innovations resulted in a 9B parameter model that performs competitively with state-of-the-art models. Our strategies are efficient and lightweight, making them easily adoptable by the community.

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