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MLLMs-Augmented Visual-Language Representation Learning

2023-11-30 18:05:52
Yanqing Liu, Kai Wang, Wenqi Shao, Ping Luo, Yu Qiao, Mike Zheng Shou, Kaipeng Zhang, Yang You


Visual-language pre-training (VLP) have achieved remarkable success in multi-modal tasks, largely attributed to the availability of large-scale image-text datasets. In this work, we demonstrate that multi-modal large language models (MLLMs) can enhance visual-language representation learning by improving data quality. Our approach is simple, utilizing MLLMs to extend multiple captions for each image. To prevent the bias that introduced by MLLMs' hallucinations and intrinsic caption styles, we propose a "text shearing" to keep the lengths of extended captions identical to the originals. In image-text retrieval, our method consistently obtains 5.6 ~ 35.0% and 16.8 ~ 46.1% improvement on R@1 under the fine-tuning and zero-shot settings, respectively. Notably, our zero-shot results are comparable to fine-tuning on target datasets, which encourages more exploration on the versatile use of MLLMs.

Abstract (translated)

视觉语言预训练(VLP)在多模态任务上的成功很大程度上归功于大型图像-文本数据集的可用性。在这项工作中,我们证明了多模态大型语言模型(MLLMs)可以通过提高数据质量来增强视觉-语言表示学习。我们的方法很简单,利用MLLMs扩展每个图像的多个摘要。为了防止MLLMs的幻觉和固有描述风格带来的偏差,我们提出了一个“文本剪切”来保持扩展摘要的长度与原始相同。在图像-文本检索中,我们的方法在微调和小幅度零样本设置下,分别获得了5.6 ~ 35.0%和16.8 ~ 46.1%的R@1改善。值得注意的是,我们的零样本结果与在目标数据集上的微调结果相当,这鼓励了更加探索MLLMs的多功能应用。



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