Anomaly detection has recently gained increasing attention in the field of computer vision,
likely due to its broad set of applications ranging from product fault detection on industrial …

Detecting abnormal human behaviors in surveillance videos is crucial for various domains,
including security and public safety. Many successful detection techniques based on deep …

A rising trend in theoretical deep learning is to understand why deep learning works through
Neural Tangent Kernel (NTK)[jgh18], a kernel method that is equivalent to using gradient …

Anomaly detection and localization of visual data, including images and videos, are of great
significance in both machine learning academia and applied real-world scenarios. Despite …

Recent advancements in language-model-based video understanding have been
progressing at a remarkable pace, spurred by the introduction of Large Language Models …

Video anomaly detection (VAD) aims at identifying unusual behaviors from videos. Most of
the existing video anomaly detection methods can achieve promising performance in the …

Nowadays there is growing demand for surveillance applications due to the safety and
security from anomalous events. An anomaly in the video is referred to as an event that has …

Unsupervised video anomaly detection (UVAD) has gained significant attention due to its
label-free nature. Typically, UVAD methods can be categorized into two branches, ie the one …

Due to the lack of anomalous data, most existing semi-supervised video anomaly detection
(SVAD) methods rely on designing self-supervised tasks to reconstruct video frames for …

Video anomaly detection is an essential yet challenging open-set task in computer vision,
often addressed by leveraging reconstruction as a proxy task. However, existing …