Technology

Vision-Language Model

Vision-Language Models (VLMs) are multimodal AI systems: they unify vision encoders (e.g., ViT) with large language models (LLMs) to jointly process and reason over image and text data.

VLMs bridge the gap between computer vision and natural language processing, enabling true cross-modal understanding. The architecture integrates a vision transformer (ViT) with a language model backbone (like LLaMA or GPT) to map visual features and text embeddings into a shared space. This fusion powers critical applications: Visual Question Answering (VQA), detailed image captioning, and complex document analysis. Key models like OpenAI's GPT-4o and open-source LLaVA demonstrate state-of-the-art performance, handling diverse inputs—images, charts, and text—to generate coherent, contextually relevant language outputs.