Proximity-CLIP: Text-Guided Semantic Proximity Learning for Zero-Shot Anomaly Detection

Vision-language models offer a promising approach for zero-shot anomaly detection (ZSAD). However, due to object-centric bias,normal and anomalous text prototypes exhibit a high semantic over-lap. While enforcing strict orthogonality between them improves dis-criminability, mapping highly contiguous visual inputs onto drasticallyorthogonal prototypes introduces a geometric dilemma, disrupting thepre-trained structural continuity. To address this problem, we proposeProximity-CLIP, a framework that visually calibrates the semantic mar-gin to guide visual adaptation. First, we introduce a visually-calibratedsemantic proximity learning mechanism that uses a bounded dynamicregularization to learn an appropriate semantic margin, ensuring dis-criminative separation while preserving structural alignment. Second, wedesign an Anomaly Query Module (AQM) driven by these text priors.Using the calibrated anomalous prototype as a semantic query, the AQMactively retrieves localized defect cues from contextual visual patches,mitigating the dilution of subtle anomalies during global pooling. Exten-sive experiments demonstrate that Proximity-CLIP outperforms currentstate-of-the-art methods across multiple ZSAD benchmarks with mini-mal architectural modifications.