Vision-Language Models: A Review of Applications and Future Directions

Abstract

 Multimodal artificial intelligence, especially Vision-Language Models (VLMs), has made significant progress in bridging the "heterogeneity gap" between visual perception and natural language understanding. This review aims to comprehensively sort out the core technologies, applications, and future challenges of vision-language models. The article first deeply explores the three key technologies supporting VLMs: multimodal representation learning aimed at building a shared semantic space, modal alignment using mechanisms such as cross-attention to achieve fine-grained correspondence, and hybrid fusion strategies that realize information synergy through in-depth interaction. This paper further outlines the wide applications of VLMs in various fields such as human-computer interaction, content creation, autonomous driving, and medical health. Finally, the article analyzes the current challenges faced by the models in terms of data dependence, interpretability, and computing costs, and looks forward to the future direction of developing next-generation models that are more efficient, controllable, and scalable.