English-based Vision-Language Pre-training (VLP) has achieved great success
in various downstream tasks. Some efforts have been taken to generalize this
success to non-English languages through Multilingual Vision-Language
Pre-training (M-VLP). However, due to the large number of languages, M-VLP
models often require huge computing resources and cannot be flexibly extended
to new languages. In this work, we propose a \textbf{M}ulti\textbf{L}ingual
\textbf{A}cquisition (MLA) framework that can easily generalize a monolingual
Vision-Language Pre-training model into multilingual. Specifically, we design a
lightweight language acquisition encoder based on state-of-the-art monolingual
VLP models. We further propose a two-stage training strategy to optimize the
language acquisition encoder, namely the Native Language Transfer stage and the
Language Exposure stage. With much less multilingual training data and
computing resources, our model achieves state-of-the-art performance on
multilingual image-text and video-text retrieval benchmarks.

本研究提出了一种 MultiLingual Acquisition（MLA）框架，该框架可以将单语言视觉语言预训练模型轻松推广到多语言环境中，并采用两阶段训练策略来优化语言获取编码器。通过更少的数据和计算资源，我们的模型在多语言图像文本和视频文本检索基准上实现了最先进的性能。