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Computer Science > Computer Vision and Pattern Recognition

arXiv:2406.08478 (cs)
[Submitted on 12 Jun 2024]

Title:What If We Recaption Billions of Web Images with LLaMA-3?

Authors:Xianhang Li, Haoqin Tu, Mude Hui, Zeyu Wang, Bingchen Zhao,
Junfei Xiao, Sucheng Ren, Jieru Mei, Qing Liu, Huangjie Zheng, Yuyin
Zhou, Cihang Xie
View a PDF of the paper titled What If We Recaption Billions of Web
Images with LLaMA-3?, by Xianhang Li and 11 other authors
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    Abstract:Web-crawled image-text pairs are inherently noisy. Prior
    studies demonstrate that semantically aligning and enriching
    textual descriptions of these pairs can significantly enhance
    model training across various vision-language tasks, particularly
    text-to-image generation. However, large-scale investigations in
    this area remain predominantly closed-source. Our paper aims to
    bridge this community effort, leveraging the powerful and \textit
    {open-sourced} LLaMA-3, a GPT-4 level LLM. Our recaptioning
    pipeline is simple: first, we fine-tune a LLaMA-3-8B powered
    LLaVA-1.5 and then employ it to recaption 1.3 billion images from
    the DataComp-1B dataset. Our empirical results confirm that this
    enhanced dataset, Recap-DataComp-1B, offers substantial benefits
    in training advanced vision-language models. For discriminative
    models like CLIP, we observe enhanced zero-shot performance in
    cross-modal retrieval tasks. For generative models like
    text-to-image Diffusion Transformers, the generated images
    exhibit a significant improvement in alignment with users' text
    instructions, especially in following complex queries. Our
    project page is this https URL

Comments: * denotes equal contributions
Subjects: Computer Vision and Pattern Recognition (cs.CV);
          Computation and Language (cs.CL)
Cite as:  arXiv:2406.08478 [cs.CV]
          (or arXiv:2406.08478v1 [cs.CV] for this version)
          https://doi.org/10.48550/arXiv.2406.08478
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Bingchen Zhao [view email]
[v1] Wed, 12 Jun 2024 17:59:07 UTC (1,642 KB)
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