Vision Language Model
A Vision Language Model (VLM) is a type of artificial intelligence model designed to seamlessly process and understand information from both visual inputs (images or videos) and textual inputs (language). Unlike traditional models that specialize in either vision or language, VLMs bridge this gap, allowing them to interpret the relationship between what an image shows and what words describe it.
VLMs represent a significant leap in multimodal AI capability. They enable machines to 'see' and 'understand' the world in a way that mirrors human perception. For businesses, this means moving beyond simple image recognition to complex contextual understanding, unlocking new levels of automation and data extraction from visual media.
The core function of a VLM involves fusing two distinct modalities—vision and language—into a unified representation space. This is typically achieved by using specialized encoders: a vision encoder (like a CNN or Vision Transformer) processes the image into a numerical embedding, and a language encoder (like a Transformer) processes the text into another embedding. These embeddings are then aligned and combined, allowing the model to perform tasks that require reasoning across both domains.
Related concepts include multimodal learning, large language models (LLMs), and computer vision systems. VLMs can be seen as an advanced integration of LLMs with powerful visual perception modules.
