Anomaly detection and localization of visual data, including images and
videos, are of great significance in both machine learning academia and applied
real-world scenarios. Despite the rapid development of visual anomaly detection
techniques in recent years, the interpretations of these black-box models and
reasonable explanations of why anomalies can be distinguished out are scarce.
This paper provides the first survey concentrated on explainable visual anomaly
detection methods. We first introduce the basic background of image-level
anomaly detection and video-level anomaly detection, followed by the current
explainable approaches for visual anomaly detection. Then, as the main content
of this survey, a comprehensive and exhaustive literature review of explainable
anomaly detection methods for both images and videos is presented. Finally, we
discuss several promising future directions and open problems to explore on the
explainability of visual anomaly detection