Vision-Language Models (VLMs) are designed to process and understand multimodal data, which includes visual information from images or videos and textual data such as descriptions or captions. To achieve this, VLMs typically use a dual encoding system. One part of the model is focused on processing images, often using convolutional neural networks (CNNs) or vision transformers. The other part processes text using recurrent neural networks (RNNs) or transformers tailored for language. By integrating outputs from both encoders, VLMs can create a unified representation that captures the relationships between visual and textual information.
For example, when a VLM is tasked with understanding a picture of a dog playing in the park, it will first analyze the image to identify features like the dog's appearance, the park setting, and objects in the background. Simultaneously, textual input such as a caption or a series of related phrases is analyzed to understand the context, actions, and attributes being described. The model then combines these insights to generate a cohesive understanding of what is happening in the image and how it relates to the text. This enables the VLM to answer questions about the content, generate relevant captions, or perform image-text alignment tasks.
VLMs rely on large datasets containing both images and their corresponding textual annotations to train effectively. This training involves teaching the model not just to recognize objects or words independently, but to understand how they interact in a given context. For instance, datasets like COCO (Common Objects in Context) include numerous images along with descriptive text, allowing the model to learn various visual concepts and their definitions effectively. As a result, once trained, VLMs can provide useful applications, such as content-based image retrieval, where users input text to find relevant images, or assistive technologies that describe scenes for visually impaired users.