Multi-label zero-shot learning

Multi-label Zero-Shot Learning (ML-ZSL) is a crucial technique in the field of computer vision, aimed at predicting multiple known and unknown labels in images. This task leverages external knowledge and semantic information to enable recognition and classification of unseen categories, significantly enhancing the model's generalization ability and broadening its application scenarios, especially in situations where data is scarce.