HIERARCHICAL PROMPT-AWARE ZERO-SHOT OUT-OF-DISTRIBUTION DETECTION

Reliable image recognition systems should both classify known categories and detect novel classes in open-set settings, especially under zero-shot constraints where no training examples are available. We propose a zero-shot OOD detection method that enriches each known label with a semantic hierarchy of fine-grained subcategories. Hierarchies are generated via structured LLM prompts and filtered with a lexical ontology for domain alignment, then integrated into CLIP to exploit coarse-to-fine semantic consistency. This training-free design improves the ability to reject inputs that do not match any known class. Experiments on standard OOD benchmarks show competitive performance and provide a more structured, interpretable prediction space.