AI RESEARCH
VID-AD: A Dataset for Image-Level Logical Anomaly Detection under Vision-Induced Distraction
arXiv CS.CV
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ArXi:2603.13964v1 Announce Type: new Logical anomaly detection in industrial inspection remains challenging due to variations in visual appearance (e.g., background clutter, illumination shift, and blur), which often distract vision-centric detectors from identifying rule-level violations. However, existing benchmarks rarely provide controlled settings where logical states are fixed while such nuisance factors vary. To address this gap, we