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The document presents LogicAD, a novel algorithm for explainable anomaly detection (AD) that leverages autoregressive multimodal Vision Language Models (AVLMs) for logical anomaly detection without the need for extensive visual annotations. LogicAD achieves state-of-the-art performance on public benchmarks, significantly outperforming existing methods in both anomaly detection accuracy and explainability. The approach integrates text feature extraction, format embedding, and a logic reasoner to effectively

Author

guosphia0329

Language

EN