VID-AD: A Dataset for Image-Level Logical Anomaly Detection under Vision-Induced Distraction

TL;DR

This paper introduces VID-AD, a new dataset for logical anomaly detection in industrial images under distraction, and proposes a language-based detection method that emphasizes logical attributes over visual noise.

Contribution

The paper provides a novel dataset with controlled variations for logical anomaly detection and introduces a language-based framework that improves detection by focusing on logical descriptions.

Findings

The dataset includes 10 scenarios with 50 tasks and over 10,000 images.

The proposed method outperforms baseline models in detecting logical anomalies.

Contrastive learning with text descriptions enhances logical attribute recognition.

Abstract

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.