This paper explores the problem of Generalist Anomaly Detection (GAD) aiming to train one
single detection model that can generalize to detect anomalies in diverse datasets from …

Unified anomaly detection (AD) is one of the most valuable challenges for anomaly
detection, where one unified model is trained with normal samples from multiple classes …

Few-shot anomaly classification (FSAC) is a vital task in manufacturing industry. Recent
methods focus on utilizing CLIP in zero/few normal shot anomaly detection instead of …

In the field of multi-class anomaly detection, reconstruction-based methods derived from
single-class anomaly detection face the well-known challenge of “learning shortcuts” …

Unsupervised reconstruction networks using self-attention transformers have achieved state-
of-the-art performance for multi-class (unified) anomaly detection with a single model …

In industrial anomaly detection, model efficiency and mobile-friendliness become the
primary concerns in real-world applications. Simultaneously, the impressive generalization …

Image anomaly detection (IAD) is an emerging and vital computer vision task in industrial
manufacturing (IM). Recently, many advanced algorithms have been reported, but their …

Existing industrial anomaly detection (IAD) methods predict anomaly scores for both
anomaly detection and localization. However, they struggle to perform a multi-turn dialog …

The unsupervised detection and localization of image anomalies hold significant importance
across various domains, particularly in industrial quality inspection. Despite its widespread …

Textile anomaly detection with high accuracy and fast frame rates are desired in real
industrial scenarios. To this end, we propose an efficient memory guided distillation network …