HyCoPro: Hyperbolic Compositional Prototypes for Compositional Zero-Shot Learning

Compositional Zero-Shot Learning (CZSL) aims to recognize unseen state–object compositions by learning from seen compositions. Recent CLIP-based methods have improved CZSL by turning each candidate pair into a text prompt, but the resulting composition prototypes are still often optimized as independent labels. This becomes fragile in open-world CZSL, where the model must score all possible compositions, many of which have no corresponding training images. To address this problem, we propose **HyCoPro**, a hyperbolic compositional closure framework for CZSL. HyCoPro organizes CLIP visual and textual features as role-specific state, object, and composition prototypes in a Poincaré ball, where Möbius addition closes primitive prototypes into composition prototypes. This closure is enforced from both visual and textual sides, strengthening unseen prototypes without requiring extra images. We further provide theoretical analysis explaining why HyCoPro is well suited to compositional recognition in CZSL. Experiments on three benchmarks demonstrate consistent gains, particularly under open-world evaluation.