UAD: A Unified Model for Zero-Shot Anomaly Detection

Abstract

Zero-shot anomaly detection (ZSAD) aims to identify and localize anomalies in previously unseen target domains without accessing any target-domain training data, which is crucial under privacy, security, or proprietary constraints. However, existing ZSAD methods often struggle to generalize across domains, as they are tightly coupled to specific object categories or rely on fragmented designs that fail to capture both semantic consistency and structural abnormality. In this paper, we propose UAD, a unified framework that addresses ZSAD from a holistic perspective by jointly modeling semantic regularity and anomaly-aware representations. The key insight of UAD is that effective ZSAD requires aligning multi-level semantic understanding with fine-grained structural cues, rather than relying solely on object-centric semantics or local appearance statistics. To this end, UAD organizes image representations into coherent semantic contexts and identifies anomalies as deviations from both local structural patterns and high-level semantic consistency. Furthermore, we enhance cross-domain robustness by improving semantic supervision and diversity through prompt concatenation and intensity-guided anomaly synthesis, enabling UAD to better generalize to unseen anomaly types and domains. Extensive experiments on 17 real-world anomaly detection datasets show that UAD achieves superior zero-shot performance of detecting and segmenting anomalies in datasets of highly diverse class semantics from various defect inspection and medical imaging domains. Our works are available at https://github.com/hanli6688/UAD