Guest Editorial: Anomaly detection and open-set recognition applications for computer vision

Abstract

Anomaly detection is a method employed to identify data points or patterns that significantly deviate from expected or normal behaviour within a dataset. This approach aims to detect observations regarded as unusual, erroneous, anomalous, rare, or potentially indicative of fraudulent or malicious activity. Open-set recognition, also referred to as open-set identification or open-set classification, is a pattern recognition task that extends traditional classification by addressing the presence of unknown or novel classes during the testing phase. This approach highlights a strong connection between anomaly detection and open-set recognition, as both seek to identify samples originating from unknown classes or distributions. Open-set recognition methods frequently involve modelling both known and unknown classes during training, allowing for the capture of the distribution of known classes while explicitly addressing the space of unknown classes. Techniques in open-set recognition may include outlier detection, density estimation, or configuring decision boundaries to better differentiate between known and unknown classes. This special issue calls for original contributions introducing novel datasets, innovative architectures, and advanced training methods for tasks related to visual anomaly detection and open-set recognition.