  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 \url{https://github.com/yangbang18/CLFM}.
