Absolute-Unified Multi-Class Anomaly Detection via Class-Agnostic Distribution Alignment
This addresses the challenge of applying anomaly detection when image categories are unavailable, offering a more practical solution for real-world scenarios, though it builds incrementally on prior unified model approaches.
The paper tackles the problem of multi-class anomaly detection without class information by proposing Class-Agnostic Distribution Alignment (CADA) to align anomaly score distributions across classes, achieving state-of-the-art results on MVTec AD and VisA datasets with significant performance improvements.
Conventional unsupervised anomaly detection (UAD) methods build separate models for each object category. Recent studies have proposed to train a unified model for multiple classes, namely model-unified UAD. However, such methods still implement the unified model separately on each class during inference with respective anomaly decision thresholds, which hinders their application when the image categories are entirely unavailable. In this work, we present a simple yet powerful method to address multi-class anomaly detection without any class information, namely \textit{absolute-unified} UAD. We target the crux of prior works in this challenging setting: different objects have mismatched anomaly score distributions. We propose Class-Agnostic Distribution Alignment (CADA) to align the mismatched score distribution of each implicit class without knowing class information, which enables unified anomaly detection for all classes and samples. The essence of CADA is to predict each class's score distribution of normal samples given any image, normal or anomalous, of this class. As a general component, CADA can activate the potential of nearly all UAD methods under absolute-unified setting. Our approach is extensively evaluated under the proposed setting on two popular UAD benchmark datasets, MVTec AD and VisA, where we exceed previous state-of-the-art by a large margin.