A Survey of Visual Sensory Anomaly Detection
It addresses the need for a thorough summary in this emerging area, which is incremental as it organizes existing knowledge without introducing new methods.
This survey provides the first comprehensive review of visual sensory anomaly detection, categorizing anomalies by form and supervision level, and summarizing challenges and open directions for the computer vision community.
Visual sensory anomaly detection (AD) is an essential problem in computer vision, which is gaining momentum recently thanks to the development of AI for good. Compared with semantic anomaly detection which detects anomaly at the label level (semantic shift), visual sensory AD detects the abnormal part of the sample (covariate shift). However, no thorough review has been provided to summarize this area for the computer vision community. In this survey, we are the first one to provide a comprehensive review of visual sensory AD and category into three levels according to the form of anomalies. Furthermore, we classify each kind of anomaly according to the level of supervision. Finally, we summarize the challenges and provide open directions for this community. All resources are available at https://github.com/M-3LAB/awesome-visual-sensory-anomaly-detection.