Transformaly - Two (Feature Spaces) Are Better Than One

Cohen, Matan Jacob; Avidan, Shai

doi:10.1109/cvprw56347.2022.00451

Cited by 6 publications

(1 citation statement)

References 15 publications

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“…Anomaly detection plays a very significant role in society. Nowadays, with the development technique of computer vision, many researchers try to address Visual Anomaly Detection tasks, such as image anomaly detection [Reiss et al, 2021;Pang et al, 2021;Roth et al, 2022;Cohen and Avidan, 2022], video anomaly detection [Georgescu et al, 2021a;Chen et al, 2022;Liu et al, 2018a;Yu et al, ;Wang et al, 2022a;Georgescu et al, 2021b]. Visual anomaly detection aims to identify unusual or abnormal patterns in visual data, such as images and videos.…”

Section: Introductionmentioning

confidence: 99%

Towards Explainable Visual Anomaly Detection

Wang¹,

Guo²,

Li³

et al. 2023

Preprint

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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.

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Section: Introductionmentioning

confidence: 99%