  Modular vision-language models (Vision-LLMs) align pretrained image encoders
with (frozen) large language models (LLMs) and post-hoc condition LLMs to
`understand' the image input. With the abundance of readily available
high-quality English image-text data as well as strong monolingual English
LLMs, the research focus has been on English-only Vision-LLMs. Multilingual
vision-language models are still predominantly obtained via expensive
end-to-end pretraining, resulting in comparatively smaller models, trained on
limited multilingual image data supplemented with text-only multilingual
corpora. We present mBLIP, the first Vision-LLM leveraging multilingual LLMs,
which we obtain in a computationally efficient manner on consumer-level
hardware. To this end, we \textit{re-align} an image encoder previously tuned
to an English LLM to a new, multilingual LLM using only a few million
multilingual training examples derived from a mix of vision-and-language tasks,
which we obtain by machine-translating high-quality English data to 95
languages. On the IGLUE benchmark and XM3600, mBLIP yields results competitive
with state-of-the-art models and it greatly outperforms strong English-only
Vision-LLMs like Llava 1.5. We release our model, code, and train data at
\url{https://github.com/gregor-ge/mBLIP}.
