Absolute-Unified Multi-Class Anomaly Detection via Class-Agnostic Distribution Alignment
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Anomaly DetectionMulti-class Anomaly DetectionUnsupervised Anomaly DetectionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Toward Multi-class Anomaly Detection: Exploring Class-aware Unified Model against Inter-class Interference
In the context of high usability in single-class anomaly detection models, recent academic research has become concerned about the more complex multi-class anomaly detection. Although several papers have designed unified…
Anomaly DetectionDecoderMulti-class Anomaly DetectionText-Guided Multimodal Unified Industrial Anomaly Detection
Industrial anomaly detection based on RGB-3D multimodal data has emerged as a mainstream paradigm for intelligent quality inspection. However, existing unsupervised methods suffer from two critical limitations: ambiguous…
Anomaly DetectionCenter-aware Residual Anomaly Synthesis for Multi-class Industrial Anomaly Detection
Anomaly detection plays a vital role in the inspection of industrial images. Most existing methods require separate models for each category, resulting in multiplied deployment costs. This highlights the challenge of dev…
Anomaly DetectionMulti-class Anomaly DetectionOmniAL: A Unified CNN Framework for Unsupervised Anomaly Localization
Unsupervised anomaly localization and detection is crucial for industrial manufacturing processes due to the lack of anomalous samples. Recent unsupervised advances on industrial anomaly detection achieve high perfor…
Anomaly DetectionAnomaly LocalizationMulti-class Anomaly DetectionA Unified Siamese Learning Framework for Zero-Day Anomaly Detection and Classification in Optical Networks
A multi-similarity Siamese neural network unifies zero-day anomaly detection and one-shot classification in optical networks, achieving over 99% accuracy and instant adaptability across lightpaths and unseen anomaly type…
Anomaly Detection