Multi Label Zero Shot Learning

Multi-label zero-shot learning (ML-ZSL) tackles the challenge of classifying data into multiple unseen categories, using only knowledge from seen classes and auxiliary information like semantic descriptions. Current research focuses on improving the accuracy of ML-ZSL by leveraging multimodal information (e.g., image and text), employing attention mechanisms to weigh different features or segments, and developing novel architectures that effectively integrate local and global features to avoid bias towards dominant classes. These advancements are significant for various applications, including image and audio classification, where handling multiple labels and unseen classes is crucial for robust and generalizable systems.