Zero-Shot Image Classification

Zero-shot image classification uses vision-language models to categorize images into arbitrary classes never seen during training — you describe categories in text, and the model matches. CLIP (2021) proved this was viable at scale by training on 400M image-text pairs, achieving competitive accuracy on ImageNet without ever seeing a labeled example. SigLIP, EVA-CLIP, and MetaCLIP have since pushed zero-shot ImageNet accuracy above 83%, closing the gap with supervised models. The paradigm shift this represents is profound: instead of collecting labeled datasets for every new domain, you just describe what you're looking for.