Year: 2025
Type: conference-paper
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 the public benchmark, MVTec LOCO AD, with an AUROC of 87.6% and an F 1 -max of 87.0% 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.
Authors: Yejin Kwon, Daeun Moon, Youngje Oh, Hyunsoo Yoon
Institution: Yonsei University
Language: English
Subfield: Industrial and Manufacturing Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Awards: G7551061356, G7992111519, G835895836
OpenAlex ID: W4412886933