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.

翻译：视觉数据（包括图像和视频）的异常检测与定位在机器学习学术界及实际应用场景中具有重大意义。尽管近年来视觉异常检测技术发展迅速，但这些黑箱模型的解释性以及异常可被准确识别的合理阐明仍较为匮乏。本文首次聚焦于可解释视觉异常检测方法进行综述。我们首先介绍了图像级异常检测与视频级异常检测的基础背景，继而阐述了当前面向视觉异常检测的可解释方法。作为本综述的核心内容，我们对图像与视频领域的可解释异常检测方法进行了全面详尽的文献梳理。最后，探讨了视觉异常检测可解释性方面若干具有前景的未来研究方向及开放性问题。