VID-AD: A Dataset for Image-Level Logical Anomaly Detection under Vision-Induced Distraction
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 introduce VID-AD, a dataset for logical anomaly detection under vision-induced distraction. It comprises 10 manufacturing scenarios and five capture conditions, totaling 50 one-class tasks and 10,395 images. Each scenario is defined by two logical constraints selected from quantity, length, type, placement, and relation, with anomalies including both single-constraint and combined violations. We further propose a language-based anomaly detection framework that relies solely on text descriptions generated from normal images. Using contrastive learning with positive texts and contradiction-based negative texts synthesized from these descriptions, our method learns embeddings that capture logical attributes rather than low-level features. Extensive experiments demonstrate consistent improvements over baselines across the evaluated settings. The dataset is available at: https://github.com/nkthiroto/VID-AD.
69bb4a60ba2b62a5fb0b0bbb
Hiroto Nakata
claimed_verified
2026-03-19T12:57:28.080Z
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nakatahiroto
nkthiroto
user
nkthiroto
69bb4a60ba2b62a5fb0b0bbc
Yawen Zou
69bb4a60ba2b62a5fb0b0bbd
Shunsuke Sakai
69bb4a60ba2b62a5fb0b0bbe
Shun Maeda
69bb4a60ba2b62a5fb0b0bbf
Chunzhi Gu
69bb4a60ba2b62a5fb0b0bc0
Yijin Wei
69bb4a60ba2b62a5fb0b0bc1
Shangce Gao
69bb4a60ba2b62a5fb0b0bc2
Chao Zhang
VID-AD: A Dataset for Image-Level Logical Anomaly Detection under Vision-Induced Distraction
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 introduce VID-AD, a dataset for logical anomaly detection under vision-induced distraction. It comprises 10 manufacturing scenarios and five capture conditions, totaling 50 one-class tasks and 10,395 images. Each scenario is defined by two logical constraints selected from quantity, length, type, placement, and relation, with anomalies including both single-constraint and combined violations. We further propose a language-based anomaly detection framework that relies solely on text descriptions generated from normal images. Using contrastive learning with positive texts and contradiction-based negative texts synthesized from these descriptions, our method learns embeddings that capture logical attributes rather than low-level features. Extensive experiments demonstrate consistent improvements over baselines across the evaluated settings. The dataset is available at: https://github.com/nkthiroto/VID-AD.
A new dataset and language-based framework for logical anomaly detection in industrial inspection, addressing challenges from visual distractions through text-description-based contrastive learning.
logical anomaly detection vision-centric detectors contrastive learning text descriptions anomaly detection framework