Detecting out-of-distribution (OOD) samples is crucial for ensuring the safety of machine
learning systems and has shaped the field of OOD detection. Meanwhile, several other …

This paper explores the problem of Generalist Anomaly Detection (GAD) aiming to train one
single detection model that can generalize to detect anomalies in diverse datasets from …

Video Anomaly Detection (VAD) finds widespread applications in security surveillance,
traffic monitoring, industrial monitoring, and healthcare. Despite extensive research efforts …

Anomaly detection (AD) plays a crucial role in many safety-critical application domains. The
challenge of adapting an anomaly detector to drift in the normal data distribution, especially …

Visual anomaly detection, the task of detecting abnormal characteristics in images, is
challenging due to the rarity and unpredictability of anomalies. In order to reliably model the …

In this paper, we propose energy-based sample adaptation at test time for domain
generalization. Where previous works adapt their models to target domains, we adapt the …

Few-shot anomaly detection for video surveillance is challenging due to the diverse nature
of target domains. Existing methodologies treat it as a one-class classification problem …

Visually-rich document entity retrieval (VDER), which extracts key information (eg date,
address) from document images like invoices and receipts, has become an important topic …

Even with a plenty amount of normal samples, anomaly detection has been considered as a
challenging machine learning task due to its one-class nature, ie, the lack of anomalous …

Time series analysis has a wide range of applications in the fields of weather forecasting,
traffic management, fault detection, intelligent operation, etc. In the real world, time series …