Robust Zero-shot Anomaly Detection under Limited Auxiliary Anomaly Priors

• URL: http://arxiv.org/abs/2606.29428v1
• Date: Sun, 28 Jun 2026 14:44:53 GMT
• Title: Robust Zero-shot Anomaly Detection under Limited Auxiliary Anomaly Priors
• Abstract summary: This study introduces DIVE, the first approach to investigate the scenario of limited auxiliary anomaly priors.<n>DIVE learns to abstract generic anomaly concepts shared across the auxiliary training domain and diverse target domains.<n> Experiments demonstrate that, under the setting of limited anomaly patterns in auxiliary data, DIVE outperforms SOTA baselines.
• Score: 10.175293570409684
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Zero-shot anomaly detection aims to identify defects in arbitrary novel domains; however, existing models assume that the auxiliary data contains a rich diversity of anomalies, neglecting the far more complex and unpredictable variations in real-world target domains. This study introduces DIVE, the first approach to investigate the scenario of limited auxiliary anomaly priors and resolve the resulting substantial performance degradation. Through a shallow-and-deep text embedding injection strategy during visual encoding, DIVE learns to abstract generic anomaly concepts shared across the auxiliary training domain and diverse target domains. Moreover, we propose a disentanglement mechanism to tackle the suboptimal alignment between visual embeddings entangled with object semantics and object-agnostic textual prompts. Experiments demonstrate that, under the setting of limited anomaly patterns in auxiliary data, DIVE outperforms SOTA baselines by up to 16.2% and 28.5% on two classification metrics, and 23.4%, 24.1%, and 47.0% on three segmentation metrics, in terms of average performance across twelve datasets. Furthermore, it maintains highly competitive performance when auxiliary data exhibits sufficient anomaly diversity.

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