Video Anomaly Detection
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Benchmarks
HR-ShanghaiTech
HR-Avenue
HR-UBnormal
ShanghaiTech
CUHK Avenue
UBnormal
ShanghaiTech Campus
UCF-Crime
UCSD Ped2
Ped2
CHAD
CHUK Avenue
IITB Corridor
Street Scene
Most implemented
Adversarially Learned One-Class Classifier for Novelty Detection
An Attribute-based Method for Video Anomaly Detection
Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learning
When, Where, and What? A New Dataset for Anomaly Detection in Driving Videos
Quo Vadis, Anomaly Detection? LLMs and VLMs in the Spotlight
Weakly-Supervised Video Anomaly Detection with Snippet Anomalous Attention
Papers
Adaptive Multi-Granularity Temporal Modeling for Weakly Supervised Video Anomaly Detection
As the scale of video surveillance data outpaces manual annotation capacities, weakly supervised video anomaly detection (WSVAD) has emerged as a critical research frontier. Most existing approaches formulate WSVAD withi…
Multiple Instance LearningVideo Anomaly DetectionEvent SegmentationA VLM Answer Is Not an Anomaly Score: Rank Compression Across Image and Video Anomaly Detection
Anomaly detection aims to identify observations that deviate from normal patterns. Recent work uses pretrained vision-language models (VLMs) for training-free image and video anomaly detection without task-specific retra…
Video Anomaly DetectionSTEP: Score-Based Temporal Energy for Human Pose Video Anomaly Detection
Skeleton-based Video Anomaly Detection (VAD) offers a robust, privacy-preserving solution for identifying abnormal behaviors. To model the distribution of normal static and moving poses, recent methods train Energy-Based…
Computational EfficiencyVideo Anomaly DetectionPose EstimationRethinking Open-World Video Anomaly Detection: Diagnosing Definition Blindness
Open-world video anomaly detection (OWVAD) is expected to detect events that match a user-specified definition of abnormality. This requirement is stronger than generic anomaly localization: in the same video, changing t…
Video Anomaly DetectionO-VAD: Industrial Video Anomaly Detection through Object-Centric Tracking and Reasoning
Industrial Video Anomaly Detection (IVAD) aims to identify anomalous objects and events in an industrial process, which is crucial for modern manufacturing and quality control systems. Existing VLM-based anomaly reasonin…
Video Anomaly DetectionStructured Evidence Selection for Weakly Supervised Video Anomaly Detection
Weakly supervised video anomaly detection relies solely on video-level labels for training, making it difficult to accurately localize anomalous events in complex scenes. In real-world videos, anomalous behaviors exhibit…
Computational EfficiencyVideo Anomaly Detection