Understanding human behavior and activity facilitates advancement of numerous real-world
applications, and is critical for video analysis. Despite the progress of action recognition …

Weakly supervised video anomaly detection (WS-VAD) is to distinguish anomalies from
normal events based on discriminative representations. Most existing works are limited in …

Abstract Weakly supervised Video Anomaly Detection (VAD) using Multi-Instance Learning
(MIL) is usually based on the fact that the anomaly score of an abnormal snippet is higher …

As a vital topic in media content interpretation, video anomaly detection (VAD) has made
fruitful progress via deep neural network (DNN). However, existing methods usually follow a …

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 …

Weakly supervised anomaly detection is a challenging task since frame-level labels are not
given in the training phase. Previous studies generally employ neural networks to learn …

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 …

Anomaly detection has been an active research area for decades, with high application
potential. Recent work has explored deep learning approaches to the detection of abnormal …

This paper proposes a novel weakly supervised approach for anomaly detection, which
begins with a relation-aware feature extractor to capture the multi-scale convolutional neural …

In this paper, we propose a scene-aware context reasoning method that exploits context
information from visual features for unsupervised abnormal event detection in videos, which …