Explainable Anomaly Detection in Images and Videos: A Survey
arxiv(2023)
摘要
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 and
video-level 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. Next, we analyze why some
explainable anomaly detection methods can be applied to both images and videos
and why others can be only applied to one modality. Additionally, we provide
summaries of current 2D visual anomaly detection datasets and evaluation
metrics. Finally, we discuss several promising future directions and open
problems to explore the explainability of 2D visual anomaly detection. The
related resource collection is given at
\href{https://github.com/wyzjack/Awesome-XAD}{this repo}.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要