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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2603.13964 |
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| _version_ | 1866914393428590592 |
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| author | Nakata, Hiroto Zou, Yawen Sakai, Shunsuke Maeda, Shun Gu, Chunzhi Wei, Yijin Gao, Shangce Zhang, Chao |
| author_facet | Nakata, Hiroto Zou, Yawen Sakai, Shunsuke Maeda, Shun Gu, Chunzhi Wei, Yijin Gao, Shangce Zhang, Chao |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_13964 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | VID-AD: A Dataset for Image-Level Logical Anomaly Detection under Vision-Induced Distraction Nakata, Hiroto Zou, Yawen Sakai, Shunsuke Maeda, Shun Gu, Chunzhi Wei, Yijin Gao, Shangce Zhang, Chao Computer Vision and Pattern Recognition 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. |
| title | VID-AD: A Dataset for Image-Level Logical Anomaly Detection under Vision-Induced Distraction |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2603.13964 |