Visual representation learning is ubiquitous in various real-world applications, including
visual comprehension, video understanding, multi-modal analysis, human-computer …

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 …

Weakly supervised video anomaly detection aims to identify abnormal events in videos
using only video-level labels. Recently, two-stage self-training methods have achieved …

Video anomaly detection (VAD) refers to the discrimination of unexpected events in videos.
The deep generative model (DGM)-based method learns the regular patterns on normal …

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 video anomaly detection aims to locate abnormal activities in untrimmed
videos without the need for frame-level supervision. Prior work has utilized graph …

Video anomaly detection (VAD) without human monitoring is a complex computer vision task
that can have a positive impact on society if implemented successfully. While recent …

Current video anomaly detection (VAD) approaches with weak supervisions are inherently
limited to a closed-set setting and may struggle in open-world applications where there can …

Out-of-distribution (OOD) detection aims to detect “unknown” data whose labels have not
been seen during the in-distribution (ID) training process. Recent progress in representation …

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