Not All Attributes Compose Equally: Family-Aware Structured Calibration for Compositional Zero-Shot Learning

Compositional zero-shot learning (CZSL) aims to recognize unseen attribute-object compositions by recombining known primitives. Existing methods mainly improve representations before pair scores are produced, but the final ranking usually does not explicitly use relations among attributes. However, attributes do not compose equally: semantically related attributes share family-level visual evidence, while their appearance and compatibility remain object dependent. We study the complementary problem of post-hoc pair calibration and introduce FASC (Family-Aware Structured Calibration), which preserves the backbone candidate space while using semantic attribute families as intermediate structure. FASC derives an image-specific family posterior by marginalizing the softmax distribution over averaged candidate-pair logits, then combines the original logits with training-frequency priors, a ranked family bias, a centered family-prior correction, and a pseudo-unseen ranking objective. On C-GQA, MIT-States, and UT-Zappos, FASC improves closed-world AUC over the same-source uncalibrated scores and exhibits low variation across five calibrator seeds. Fixed-source ensemble, component, and taxonomy ablations isolate the contribution of semantic family structure, while evaluations on two additional CZSL backbones demonstrate transfer beyond the main backbone.