Semi-Supervised Anomaly Detection in Electronic-Exam Proctoring Based on Skeleton Similarity

Semantic Coarse-to-Fine Granularity Learning for Two-Stage Few-Shot Anomaly Detection

“…These methods, which only use normal samples during training, identify anomalies by contrasting the tested data with learned normal features (Ilyas et al, 2022;Salehi et al, 2021;Xu et al, 2023). While unsupervised anomaly detection methods (Sun et al, 2023;Fang et al, 2023) primarily focus on feature learning to capture normal data's intrinsic characteristics, recent approaches allow for the labeling of a small number of anomalous samples (Atabay & Hassanpour, 2023), albeit at an increased cost.…”

“…To address the challenges of limited sample images and reduce labeling costs, few-shot anomaly detection has been proposed. Conventional supervised anomaly detection relies on a combination of limited anomaly data and a large number of normal samples to detect anomalies (Ding et al, 2022;Atabay & Hassanpour, 2023;Bozorgtabar & Mahapatra, 2023;Pang, Yan, et al,2020), as shown in Figure 1(a), but it often exhibits inferior performance compared to unsupervised methods in anomaly identification and localization. In contrast, embedding-based, unsupervised anomaly detection methods leverage pre-trained models (Wang et al 2023) eliminating the need for a large amount of training data, as shown in Figure 1(b).…”