Multilingual Vision

Multilingual vision research aims to develop artificial intelligence systems that can understand and interact with visual information across multiple languages. Current efforts focus on adapting existing vision-language models, like CLIP, to multilingual contexts, often employing techniques such as knowledge distillation, continual learning, and contrastive learning to improve efficiency and cross-lingual generalization. These advancements are crucial for bridging the language gap in multimodal AI, enabling applications such as improved image retrieval, visual question answering, and broader accessibility of AI-powered tools across diverse linguistic communities.