Perceptual Anchoring: Prototype-Guided Text Calibration for Training-free Open-Vocabulary Semantic Segmentation

This paper proposes Prototype-Guided Text Calibration (PTC) for training-free open-vocabulary semantic segmentation. PTC first selects reliable visual evidence from initial image-text matching scores to build category-specific prototypes, then uses those prototypes to calibrate text embeddings with evidence-dependent strength. The authors state that PTC requires no additional training or external models and can be added to existing methods. Experiments reportedly cover eight benchmarks and six representative methods, but the supplied abstract provides neither method names nor numerical gains. The arXiv record is dated August 4, 2026 and therefore requires independent verification.