Granularity-Aware Evidence Aggregation and Calibration for Training-Free Zero-Shot Multi-Label Recognition

CLIP provides strong zero-shot recognition capabilities, but its global representation may overlook small or spatially localized objects in multi-label images. Local crops can recover such evidence, yet may also amplify responses to absent classes, including semantically related alternatives. We introduce GRADE (Granularity-Aware Regional Aggregation and Discriminative Evidence Calibration), a training-free framework for zero-shot multi-label image recognition. GRADE aggregates class-specific regional responses across multiple context granularities, using class-wise global-score percentiles to guide aggregation. When local evidence dominates, comparison with semantically neighboring classes downweights ambiguous local responses before global-local fusion. Experiments on COCO and VG-256 demonstrate competitive performance against existing training-free methods, with consistent mAP improvements over global CLIP across both benchmarks. Ablation studies further confirm the effectiveness of aggregating local evidence across multiple regions and context granularities.