How Vision-Language Models Process Images and Text Together

Introduction

Modern AI models like GPT-4V, Claude, and Gemini can look at an image and describe it, answer questions about it, or reason about what's happening in it — all in natural language. These are called Vision-Language Models (VLMs), and they represent one of the most exciting frontiers in AI: teaching machines to understand images and text as a unified system, rather than two separate skills bolted together.

This post breaks down how VLMs actually work — how an image becomes something a language model can "read," and how the two modalities are fused together to produce coherent, grounded responses.

The Core Challenge

Text and images are fundamentally different types of data:

Text  -> discrete symbols (words/tokens) in a sequence
Image -> continuous pixel values in a 2D grid

A language model only understands sequences of tokens. So the central problem VLMs solve is:

"How do we turn an image into something that looks like a sequence
 of tokens, so a language model can process it the same way it
 processes text?"

Step 1: Encoding the Image

The first step is converting the raw image into a numerical representation the model can use. This is typically done with a vision encoder — most commonly a Vision Transformer (ViT).

How it works: 1. The image is split into fixed-size patches (e.g., 16x16 pixels) 2. Each patch is flattened and converted into a vector (embedding) 3. These patch embeddings are treated like a sequence — similar to how words are treated as a sequence of token embeddings

Example: A 224x224 image split into 16x16 patches produces 196 patches, each becoming its own embedding vector — similar to 196 "image tokens."

Image (224x224) -> [Patch 1, Patch 2, ..., Patch 196]
                 -> [Vector 1, Vector 2, ..., Vector 196]

This is the same core idea as CLIP and other vision transformer models: turn pixels into a sequence of meaningful vectors.

Step 2: Aligning Vision and Language Spaces

Here's the tricky part: the vision encoder and the language model were often trained separately, on different data, and produce embeddings that live in different "spaces." A raw image embedding doesn't automatically mean anything to a language model.

To fix this, VLMs use a connector (also called a projector or adapter) that maps image embeddings into the same embedding space the language model expects.

Common approaches:

1. Linear/MLP Projection
   A simple neural network layer that transforms image embeddings
   into the same dimensionality and "meaning space" as text token
   embeddings.

2. Cross-Attention Layers
   Instead of directly injecting image tokens into the text sequence,
   the model uses attention layers where text tokens can "look at"
   image tokens and pull relevant visual information as needed.

3. Q-Former (Query Transformer)
   Used in models like BLIP-2, this compresses a large number of
   image patch embeddings into a smaller, fixed set of "query" tokens
   that summarize the image efficiently before passing them