Hierarchical Label

Hierarchical label classification tackles the challenge of assigning data points to categories organized in a tree-like structure, reflecting inherent semantic relationships between labels. Current research focuses on improving accuracy and efficiency, particularly in few-shot learning scenarios, using techniques like retrieval-based methods, graph-based label relationship modeling, and prompt tuning with large language models. These advancements are impacting various fields, including image segmentation, fault detection, and human activity recognition, by enabling more nuanced and accurate classification of complex data. The development of robust hierarchical classification methods is crucial for handling increasingly complex datasets with intricate label structures.