Video anomaly detection is well investigated in weakly supervised and one-class
classification (OCC) settings. However, unsupervised video anomaly detection is quite …

Detecting abnormal events in video is commonly framed as a one-class classification task,
where training videos contain only normal events, while test videos encompass both normal …

We present a new egocentric procedural error dataset containing videos with various types
of errors as well as normal videos and propose a new framework for procedural error …

A self-supervised multi-task learning (SSMTL) framework for video anomaly detection was
recently introduced in literature. Due to its highly accurate results, the method attracted the …

We present a novel method for weakly-supervised action segmentation and unseen error
detection in anomalous instructional videos. In the absence of an appropriate dataset for this …

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 …

Class imbalance is a common challenge in real-world recognition tasks, where the majority
of classes have few samples, also known as tail classes. We address this challenge with the …

Anomaly detection (AD) is a crucial task in machine learning with various applications, such
as detecting emerging diseases, identifying financial frauds, and detecting fake news …

Most cross-domain unsupervised Video Anomaly Detection (VAD) works assume that at
least few task-relevant target domain training data are available for adaptation from the …

With the popularity of video surveillance technology, people are paying more and more
attention to how to detect abnormal states or events in videos in time. Therefore, real-time …