Semantic Hierarchy-Aware Hyperbolic Representations for Multi-Label Classification With Single Positive Labels

Single positive multi-label learning (SPML) aims to recognize multiple categories with limited supervision from one positive label in an image. With the emergence of pre-trained visual-language models such as CLIP, recent studies focused on capturing label-to-label dependencies. However, hierarchies with deeper layers of labels or more branches in label-to-label relationships cannot be well expressed in Euclidean space. To address the challenge, we introduce a semantic hierarchy-aware hyperbolic representations framework for single positive multi-label learning. Specifically, drawing inspiration from semantic hierarchical information, we introduce a label relation prior strategy to map single labels to other labels. The semantic chain of labels is extracted along the hierarchical path from the child node to the parent node. Furthermore, hyperbolic entailment constraints are adopted to enforce the semantic similarity between image-text pairs and the hierarchical consistency among labels in hyperbolic space. Experimental results conducted on four SPML benchmark datasets demonstrate that our SHHNet achieves state-of-the-art performance.