Current pre-trained vison-language models (PVLMs) achieve excellent
performance on a range of multi-modal datasets. Recent work has aimed at
building multilingual models, and a range of novel multilingual multi-modal
datasets have been proposed. Current PVLMs typically perform poorly on these
datasets when used for multi-modal zero-shot or few-shot cross-lingual
transfer, especially for low-resource languages. To alleviate this problem, we
propose a novel meta-learning fine-tuning framework. Our framework makes
current PVLMs rapidly adaptive to new languages in vision-language scenarios by
designing MAML in a cross-lingual multi-modal manner. Experiments show that our
method boosts the performance of current state-of-the-art PVLMs in both
zero-shot and few-shot cross-lingual transfer on a range of vision-language
understanding tasks and datasets (XVNLI, xGQA, MaRVL, xFlicker&Co

本文提出的元学习微调框架可提高目前状态下的预训练视觉语言模型在跨语言多模态场景下的适应性和性能表现，实验证明该方法在跨语言零样本和少样本多模态交叉迁移的场景下均可提高模型性能。