Deep Visual Anomaly Detection and Localization

Abstract

Unsupervised visual anomaly detection and localization are fundamental challenges in computer vision, aimed at identifying unknown anomalous patterns. We focus on novel deep learning technologies for visual anomaly detection, addressing the challenges of limited and unknown data in applications such as industrial inspection, medical diagnosis, and video surveillance. These application scenarios have stringent requirements on the performance of anomaly detection algorithms, especially in terms of accuracy, scalability, and generalization ability. With the development of deep learning techniques uncovering great potential in visual anomaly detection, we propose a series of specific deep visual anomaly detection frameworks for this problem. We first propose reverse teacher-student networks for anomaly detection, where the student network learns to predict normal patterns from a teacher network. By reversing the teacher-student comparative flow, we enhance feature discrepancies between the networks for unknown abnormalities, improving the one-class anomaly detection performance. Additionally, we propose structural normality learning to overcome the cross-class interference issue, allowing a single teacher-student model to detect anomalies across multiple categories, improving scalability and generalization of our proposed models. We then investigate zero-shot anomaly detection, where the goal is to identify anomalies without prior training on normal data. By leveraging contrastive vision-language networks, we introduce domain-aware textual prompts and anomaly-oriented test-time adaptation strategy to guide anomaly detection and localization. Furthermore, we propose an open-world anomaly detection framework, enabling detection and localization of anomalies in both known and unknown objects via the adaptive fusion of the prompting vision-language and reverse teacher-student networks. In addition to natural image objects, we also explore the application of visual anomaly detection in video surveillance and medical imaging, two domains with unique challenges. For video anomaly detection, we address the spatiotemporal dependencies in video sequences, improving anomaly detection by modeling temporal continuity. In medical imaging, we focus on the specialized imaging structures to improve anomaly detection methods in challenging medical diagnostic settings. Since previous anomaly detection methods have limitations in scalability, we propose novel methods tailored to the specific scenarios of video and medical applications. In summary, this thesis advances visual anomaly detection by proposing novel algorithms for one-class, multi-class, zero-shot, and open-world anomaly detection and localization. We also explore the expanding applications of anomaly detection in industrial, video and medical domains. Our study paves the way for more robust and scalable anomaly detection systems in real-world applications