While vision-language pre-trained models (VL-PTMs) have advanced multimodal
research in recent years, their mastery in a few languages like English
restricts their applicability in broader communities. To this end, there is an
increasing interest in developing multilingual VL models via a joint-learning
setup, which, however, could be unrealistic due to expensive costs and data
availability. In this work, we propose to extend VL-PTMs' language capacity by
continual language learning (CLL), where a model needs to update its linguistic
knowledge incrementally without suffering from catastrophic forgetting (CF). We
begin our study by introducing a model dubbed CLL-CLIP, which builds upon CLIP,
a prevailing VL-PTM that has acquired image-English text alignment.
Specifically, CLL-CLIP contains an expandable token embedding layer to handle
linguistic differences. It solely trains token embeddings to improve memory
stability and is optimized under cross-modal and cross-lingual objectives to
learn the alignment between images and multilingual texts. To alleviate CF
raised by covariate shift and lexical overlap, we further propose a novel
approach that ensures the identical distribution of all token embeddings during
initialization and regularizes token embedding learning during training. We
construct a CLL benchmark covering 36 languages based on MSCOCO and XM3600
datasets and then evaluate multilingual image-text retrieval performance.
Extensive experiments verify the effectiveness of CLL-CLIP and show that our
approach can boost CLL-CLIP, e.g., by 6.7% in text-to-image average Recall@1 on
XM3600, and improve various state-of-the-art methods consistently. Our code and
data are available at https://github.com/yangbang18/CLFM.

通过连续语言学习 (CL) 扩展视觉 - 语言预训练模型 (VL-PTMs) 的语言能力，并提出了 CLL-CLIP 模型，其通过仅训练标记嵌入来改善内存稳定性，并通过跨模态和跨语言目标进行优化以学习图像和多语种文本之间的对齐关系，实验证明该方法在多语言图像 - 文本检索性能上具有有效性。