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Anomaly Detection On Ubnormal

Metrics

AUC

RBDC

TBDC

Results

Performance results of various models on this benchmark

				Paper Title	Repository
STG-NF - Supervised	79.2%	-	-	Normalizing Flows for Human Pose Anomaly Detection
MULDE-frame-centric-micro-one-class-classification	72.8%	-	-	MULDE: Multiscale Log-Density Estimation via Denoising Score Matching for Video Anomaly Detection
AnomalyRuler	71.9%	-	-	Follow the Rules: Reasoning for Video Anomaly Detection with Large Language Models
STG-NF - Unsupervised	71.8%	-	-	Normalizing Flows for Human Pose Anomaly Detection
TimeSformer	68.5%	0.04	0.05	Is Space-Time Attention All You Need for Video Understanding?
MoCoDAD	68.3%	-	-	Multimodal Motion Conditioned Diffusion Model for Skeleton-based Video Anomaly Detection
COSKAD-hyperbolic	65%	-	-	Contracting Skeletal Kinematics for Human-Related Video Anomaly Detection
COSKAD-euclidean	64.9%	-	-	Contracting Skeletal Kinematics for Human-Related Video Anomaly Detection
COSKAD-radial	62.9%	-	-	Contracting Skeletal Kinematics for Human-Related Video Anomaly Detection
FPDM	62.7	-	-	Feature Prediction Diffusion Model for Video Anomaly Detection	-
SSMTL++v1	62.1%	25.63	63.53	SSMTL++: Revisiting Self-Supervised Multi-Task Learning for Video Anomaly Detection	-
Background-Agnostic Framework	61.3%	25.43	56.27	A Background-Agnostic Framework with Adversarial Training for Abnormal Event Detection in Video
BiPOCO	50.7	-	-	BiPOCO: Bi-Directional Trajectory Prediction with Pose Constraints for Pedestrian Anomaly Detection
MIL	50.3%	0.002	0.001	Real-world Anomaly Detection in Surveillance Videos

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Anomaly Detection On Ubnormal | SOTA | HyperAI