How Vision Language Models Process Images Alongside Text Explained

Vision Language Models (VLMs) are AI systems capable of processing both images and text, unlike standard Large Language Models that handle text alone. A VLM typically combines three core components: a vision encoder, a connector or projector, and a language model working in sequence. The vision encoder converts an image into numerical representations by analyzing visual patterns, shapes, objects, and spatial relationships, often by dividing the image into smaller patches. Two key capabilities that emerge from this process are perception — understanding the overall visual content and context — and grounding, which links specific language references to particular regions within an image. VLMs are increasingly used in AI applications where understanding visual input alongside natural language queries is essential.