LogicQA：利用视觉语言模型生成问题的逻辑异常检测
LogicQA: Logical Anomaly Detection with Vision Language Model Generated Questions
AI总结：
本研究提出无需训练、无需标注的少样本LogicQA框架，通过视觉语言模型生成问题构建检查清单识别逻辑异常并提供解释，在公开基准和工业数据上均取得优异性能。
摘要：
异常检测（AD）专注于检测与标准模式存在差异的样本，是过程控制中的重要工具。逻辑异常可能在视觉上表现正常，但违反了关于物体存在、排列或数量的预定义约束，依赖于推理和可解释性。我们提出了LogicQA框架，该框架通过为工业操作人员提供逻辑异常的解释来增强异常检测能力。LogicQA将自动生成的问题编译成检查清单，并收集响应以识别逻辑约束的违反情况。LogicQA无需训练、无需标注，可在少样本设置下运行。我们在公开基准MVTec LOCO AD上取得了当前最优（SOTA）的逻辑异常检测性能，AUROC达87.6%，F1最大值达87.0%，同时还能提供异常解释。此外，我们的方法在半导体SEM企业数据上也表现出优异性能，进一步验证了其在工业应用中的有效性。
英文摘要：
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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