How Vision-Language Models (VLMs) Work

Vision-language models (VLMs) extend the capabilities of language models beyond text by allowing them to understand and reason about images. Instead of treating vision and language as separate problems, VLMs combine visual perception with linguistic reasoning inside a single model. This enables systems that can describe images, answer questions about visual content, and engage in multimodal conversations.

Traditional language models operate on sequences of text tokens, while vision models operate on pixel grids. These representations are fundamentally different, which raises an important question: how can a model reason about images using the same mechanisms it uses for language? Vision-language models address this by transforming visual information into a form that language models can process.

In this lesson, we will explore how vision-language models combine vision and language, how visual data is converted into representations compatible with language models, and the capabilities that emerge from this integration.

Combining vision and language

At the core of a vision-language model is a simple idea: images and text must be combined within the same model in a compatible form. However, this does not mean that images and text are treated the same way from the start. Instead, each modality is first processed using techniques suited to its structure, and only then are they combined.

Images are rich, high-dimensional signals composed of pixels arranged in two-dimensional space. Text, by contrast, is discrete and sequential. Because of this mismatch, VLMs do not feed raw images directly into language models. Doing so would overwhelm the model and violate the assumptions on which language models rely.

Instead, VLMs use a vision encoder to process images and a language model to handle text. These components play different roles but work together to enable multimodal understanding.

The role of the vision encoder

The vision encoder extracts meaningful visual features from an image. This encoder might be a convolutional neural network (CNN) or a Vision Transformer (ViT), but its goal is the same: convert pixels into a set of numerical representations that capture objects, textures, spatial relationships, and other visual cues.

Rather than producing a single label or description, the vision encoder outputs a collection of embeddings that represent different parts or aspects of the image. These embeddings summarize what is present in the image and where it appears, without committing to words yet. ...