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Contrastive Language-Image Pre-Training is a machine learning technique that involves training a model to understand and relate language (text) and image data through a process of contrastive learning. This approach encourages the model to learn how to associate text and images in a meaningful way by contrasting positive pairs (correct associations) against negative pairs (incorrect associations). During training, the model learns to embed both text and image data in a shared space, making it capable of performing tasks that require connecting and understanding text and images together, such as image captioning or visual question-answering. This technique has shown promise in bridging the gap between textual and visual information, facilitating applications in computer vision and natural language understanding.