This survey examines multilingual vision-language models that process text and images across languages. We review 33 models and 23 benchmarks, spanning encoder-only and generative architectures, and identify a key tension between language neutrality (consistent cross-lingual representations) and cultural awareness (adaptation to cultural contexts). Current training methods favor neutrality through contrastive learning, while cultural awareness depends on diverse data. Two-thirds of evaluation benchmarks use translation-based approaches prioritizing semantic consistency, though recent work incorporates culturally grounded content. We find discrepancies in cross-lingual capabilities and gaps between training objectives and evaluation goals.
@article{arxiv.2509.22123,
title = {Multilingual Vision-Language Models, A Survey},
author = {Andrei-Alexandru Manea and Jindřich Libovický},
journal= {arXiv preprint arXiv:2509.22123},
year = {2026}
}