CLIP (Contrastive Language–Image Pretraining) is a machine learning model developed by OpenAI that connects visual and textual understanding. It bridges the gap between images and text by learning to associate them through a contrastive learning approach. CLIP is trained on a vast dataset of image-text pairs, enabling it to recognize the relationships between visuals and their corresponding descriptions without relying on labels for specific tasks.
At its core, CLIP employs two neural networks: one processes images, while the other processes text. These networks embed images and text into a shared high-dimensional space, where related pairs are positioned closer together, and unrelated ones are further apart. This allows CLIP to perform zero-shot learning, meaning it can handle tasks it wasn't explicitly trained for, simply by using natural language descriptions.
Developers use CLIP for a variety of applications, including image classification, retrieval, and multimodal tasks that require understanding both text and visuals. For example, it can identify objects in images based on descriptive prompts or retrieve images matching specific textual descriptions. Its versatility and ability to generalize make CLIP a powerful tool for creating applications that integrate vision and language, such as advanced search engines, creative AI tools, and content moderation systems.