Vision-Language Models (VLMs) are types of artificial intelligence systems that can process and understand visual data (like images or videos) alongside textual data (such as descriptions or questions). These models combine elements from both computer vision and natural language processing to create a framework that can perform tasks requiring both types of information. For instance, a VLM can analyze an image and provide a textual description or answer questions related to what it sees in the image.
A key feature of VLMs is their ability to learn from large datasets that consist of paired images and captions. By training on this kind of data, a VLM can learn how to connect visual elements with linguistic concepts. This enables it to perform various tasks, like image captioning, where it generates a textual description of an image, or visual question answering, where it can provide answers to questions about an image. Examples of popular VLMs include CLIP (Contrastive Language-Image Pre-training) from OpenAI, which can recognize and relate images and text, and DALL-E, which generates images from textual descriptions.
Developers can leverage VLMs in numerous applications across different domains. For instance, in e-commerce, VLMs can enhance product search by allowing users to query items using images instead of text. In accessibility, they can help visually impaired users by providing spoken descriptions of images on the web. In the field of education, VLMs can support interactive learning by allowing students to ask questions about images, fostering a more engaging learning experience. Overall, VLMs represent a significant step towards creating more intuitive and versatile AI systems that better understand the interplay between visual and textual information.