Prototype-Conditioned Imagination for Compositional Zero-Shot Learning

Compositional Zero-Shot Learning (CZSL) requires learningto compose primitive concepts from seen data and generalize to unseenattribute–object pairs. Existing methods typically disentangle visual fea-tures into isolated primitive components and align them with their cor-responding text concepts or full compositions. However, they often suf-fer from ambiguous vision–language alignment and lack mechanisms toexplicitly capture cross-primitive semantic interactions, ultimately hin-dering generalization to unseen compositions. To overcome these chal-lenges, we introduce Prototype-Conditioned Imagination (PCI), a two-stage framework. In the first stage, Bidirectional Alignment and Disen-tanglement (BAD) couples cross-modal attention with entropic optimal-transport regularization to effectively align visual and textual represen-tations while enforcing global disentanglement of attribute and objectprimitives, thereby mitigating ambiguous vision-language matching. Inthe second stage, Prototype-driven Compositional Modulation (PCM)explicitly models how attribute semantics modulate object features tosynthesize compositional representations, making cross-primitive rela-tions explicit and substantially improving recognition of unseen compo-sitions. Evaluated under both closed-world and open-world protocols onfour popular benchmarks, our approach achieves state-of-the-art perfor-mance, demonstrating superior generalization capabilities to unseen com-positions. Our code is available at https://github.com/YFan-Z1/PCI.