Traffic accident detection and anticipation is an obstinate road safety problem and
painstaking efforts have been devoted. With the rapid growth of video data, Vision-based …

Weakly supervised detection of anomalies in surveillance videos is a challenging task.
Going beyond existing works that have deficient capabilities to localize anomalies in long …

Increasing scene-awareness is a key challenge in video anomaly detection (VAD). In this
work, we propose a hierarchical semantic contrast (HSC) method to learn a scene-aware …

The recent contrastive language-image pre-training (CLIP) model has shown great success
in a wide range of image-level tasks, revealing remarkable ability for learning powerful …

Learning discriminative features for effectively separating abnormal events from normality is
crucial for weakly supervised video anomaly detection (WS-VAD) tasks. Existing …

Image-level weakly supervised semantic segmentation (WSSS) is a fundamental yet
challenging computer vision task facilitating scene understanding and automatic driving …

As essential tools for industry safety protection, automatic video anomaly detection systems
(AVADS) are designed to detect anomalous events of concern in surveillance videos …

Surveillance cameras are installed across various sectors of a smart city in order to capture
ongoing events for monitoring purposes. The analysis of these surveillance videos is an …

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 …

Abstract ecent progress in video anomaly detection suggests that the features of
appearance and motion play crucial roles in distinguishing abnormal patterns from normal …