Video Anomaly Detection (VAD) serves as a pivotal technology in the intelligent surveillance
systems, enabling the temporal or spatial identification of anomalous events within videos …

Transformer has shown excellent performance in remote sensing field with long-range
modeling capabilities. Remote sensing video (RSV) moving object detection and tracking …

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 …

Abstract Weakly Supervised Video Anomaly Detection (WSVAD) is challenging because the
binary anomaly label is only given on the video level, but the output requires snippet-level …

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) aims at localizing unexpected actions or activities in a video
sequence. Existing mainstream VAD techniques are based on either the one-class …

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 …

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 …

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