Pixel-Level Anomaly Detection via Uncertainty-aware Prototypical Transformer

Pixel-level visual anomaly detection, which aims to recognize the abnormal areas from images, plays an important role in industrial fault detection and medical diagnosis. However, it is a challenging task due to the following reasons: i) the large variation of anomalies; and ii) the ambiguous boundary between anomalies and their normal surroundings. In this work, we present an uncertainty-aware prototypical transformer (UPformer), which takes into account both the diversity and uncertainty of anomaly to achieve accurate pixel-level visual anomaly detection. To this end, we first design a memory-guided prototype learning transformer encoder to learn and memorize the prototypical representations of anomalies for enabling the model to capture the diversity of anomalies. Additionally, an anomaly detection uncertainty quantizer is designed to learn the distributions of anomaly detection for measuring the anomaly detection uncertainty. Furthermore, an uncertainty-aware transformer decoder is proposed to leverage the detection uncertainties to guide the model to focus on the uncertain areas and generate the final detection results. As a result, our method achieves more accurate anomaly detection by combining the benefits of prototype learning and uncertainty estimation. Experimental results on five datasets indicate that our method achieves state-of-the-art anomaly detection performance.