Natural language supervision at scale produces visual representations that generalize to arbitrary image classification tasks without any task-specific training.
Introduces CLIP, which learns visual representations from natural language supervision by training on 400M image-text pairs. The resulting model performs zero-shot image classification competitive with task-specific supervised models.