Learning Transferable Visual Models From Natural Language Supervision
The paper presents CLIP, which learns visual representations by contrastively matching images to their natural-language captions over a 400-million-pair web dataset. The pretrained model can be applied zero-shot to many downstream vision tasks by framing class labels as text prompts, without task-specific fine-tuning. It matches the accuracy of a supervised ImageNet ResNet-50 zero-shot and transfers robustly across a broad benchmark suite.