Zero-Shot Transfer Learning

Zero-shot learning is a machine learning paradigm where a model generalizes to new tasks or classes without ever seeing training examples of those tasks. Instead, it uses semantic knowledge: descriptions, attributes, or relationships. For example, a zero-shot model trained on common animals can classify a "zebra" (described as "a horse with stripes") without ever seeing a zebra photo during training. The approach leverages semantic embeddings, knowledge graphs, and multimodal learning (combining vision, text, and other modalities). Models like CLIP (Contrastive Learning of Image and Point clouds) learn aligned embeddings for images and text, enabling zero-shot classification by comparing image embeddings to text descriptions of classes.