RA-CLIP, short for Retrieval Augmented Contrastive Language-Image Pre-Training, is a multimodal artificial intelligence framework that enhances vision-language models by retrieving relevant image-text data to augment representations during learning and inference. Unlike standard contrastive models that rely purely on internal parameters to memorize vast amounts of visual concepts from scratch, RA-CLIP utilizes an external reference repository of paired visual and textual examples to help align cross-modal features. By leveraging this external knowledge base to provide context for input images, the framework reduces the memorization burden of the network while improving accuracy and sample efficiency on downstream visual recognition tasks such as zero-shot image classification.
