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:尽管近年来视觉异常探测技术的迅速发展,但对这些黑盒模型的解释和对异常现象可加以区分的合理解释仍然很少。本文提供了第一次调查,重点是可解释的视觉异常现象探测方法。我们首先介绍图像级别异常现象探测和视频级别异常现象探测的基本背景,然后介绍目前可解释的视觉异常现象探测方法。随后,作为这次调查的主要内容,对图像和视频可解释的异常现象探测方法进行了全面和详尽的文献审查。最后,我们讨论了一些有希望的未来方向和公开的问题,以探讨视觉异常现象探测的可解释性。