HyGLoA: Dual-Granularity Hyperbolic Modeling with Local Geodesic-Ray Alignment for Zero-Shot Learning

Zero-shot learning (ZSL) recognizes unseen classes by transferring knowledge from auxiliary semantics. Existing methods have explored various visual-semantic alignment schemes at either attribute or class granularity, yet most perform in Euclidean spaces. The polynomial volume growth of Euclidean space makes it difficult to allocate increasing capacity to finer semantic levels, while its geometry does not naturally encode the asymmetric specificity relations among classes, attributes, and visual evidence. We introduce HyGLoA (Hyperbolic Global–Local Alignment), a dual-granularity framework that represents global class identity and local attribute evidence in two independent hyperbolic spaces under the Lorentz model. The global branch aligns the image CLS token with class-name prototypes, whereas the local branch projects patch tokens, aggregates them via Lorentzian centroid pooling, and aligns the resulting representation with attribute prototypes. Both branches use branch-specific direction–radius decoupled projections to control semantic direction and geodesic radius separately. To capture the asymmetric relation between partial local evidence and complete attribute semantics, we derive local geodesic-ray alignment from the zero-aperture limit of hyperbolic entailment. It removes cone-angle tolerance and penalizes deviation of the pooled local representation from the matched prototype's origin-facing geodesic ray. We further perform prototype bundle expansion on normalized semantic prototypes before hyperbolic lifting, improving angular discrimination without interfering with radial modeling. The two branch distributions undergo probability-level fusion for final prediction. Experiments on three popular benchmarks yield the highest generalized-ZSL harmonic mean among the compared methods, while ablations validate the principal design choices.