LogicQA: Logical Anomaly Detection with Vision Language Model Generated Questions

• URL: http://arxiv.org/abs/2503.20252v1
• Date: Wed, 26 Mar 2025 05:38:45 GMT
• Title: LogicQA: Logical Anomaly Detection with Vision Language Model Generated Questions
• Authors: Yejin Kwon, Daeun Moon, Youngje Oh, Hyunsoo Yoon,
• Abstract summary: We introduce LogicQA, a framework that enhances Anomaly Detection (AD)<n> LogicQA compiles automatically generated questions into a checklist and collects responses to identify violations of logical constraints.<n>We achieve state-of-the-art (SOTA) Logical AD performance on public benchmarks, MVTec LOCO AD, with an AUROC of 87.6 percent and an F1-max of 87.0 percent along with the explanations of anomalies.
• Score: 4.63822109539229
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Anomaly Detection (AD) focuses on detecting samples that differ from the standard pattern, making it a vital tool in process control. Logical anomalies may appear visually normal yet violate predefined constraints on object presence, arrangement, or quantity, depending on reasoning and explainability. We introduce LogicQA, a framework that enhances AD by providing industrial operators with explanations for logical anomalies. LogicQA compiles automatically generated questions into a checklist and collects responses to identify violations of logical constraints. LogicQA is training-free, annotation-free, and operates in a few-shot setting. We achieve state-of-the-art (SOTA) Logical AD performance on public benchmarks, MVTec LOCO AD, with an AUROC of 87.6 percent and an F1-max of 87.0 percent along with the explanations of anomalies. Also, our approach has shown outstanding performance on semiconductor SEM corporate data, further validating its effectiveness in industrial applications.

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