Vision-Language Models

Multimodal AI systems that seamlessly fuse computer vision and natural language processing, enabling models to 'see' and 'reason' about visual data using text.

Vision-Language Models (VLMs) are multimodal AI architectures that integrate a vision encoder (e.g., ViT, CLIP) and a language model (e.g., LLaMA, GPT) via an alignment layer. This setup allows them to jointly process and understand both image and text inputs, bridging the gap between pixels and semantic meaning. Key applications include Visual Question Answering (VQA), sophisticated image captioning, and object grounding. State-of-the-art models like LLaVA and Qwen3-VL demonstrate robust zero-shot capabilities, executing complex tasks by simply following natural language instructions.

https://www.encord.com/blog/guide-to-vision-language-models-vlms/