  Visual-language pre-training has achieved remarkable success in many
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
establishing richer image-text associations for image-text datasets. Our
approach is simple, utilizing MLLMs to extend multiple diverse captions for
each image. To prevent the bias introduced by MLLMs' hallucinations and
monotonous language styles, we propose "text shearing" to maintain the quality
and availability of extended captions. In image-text retrieval, without
introducing additional training cost, our method consistently obtains 5.6 ~
35.0 and 16.8 ~ 46.1 improvement on Recall@1 under the fine-tuning and
zero-shot settings, respectively. Notably, we obtain zero-shot results that are
comparable to fine-tuning on target datasets, which encourages more exploration
of the versatile use of MLLMs.
