Papers by Youngje Oh
LogicQA: Logical Anomaly Detection with Vision Language Model Generated Questions (2025.acl-industry)
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| Challenge: | Anomaly Detection (AD) focuses on detecting samples that differ from the standard pattern, making it vital for quality control and process optimization. |
| Approach: | They propose a framework that provides industrial operators with explanations for logical anomalies by compiling automatically generated questions into a checklist and collecting responses. |
| Outcome: | The proposed framework achieves state-of-the-art (SOTA) Logical AD performance on public benchmarks, MVTec LOCO AD, with an AUROC of 87.6% and an F1-max of 88.0% along with the explanations of anomalies. |