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Anomaly Detection On Shanghaitech

평가 지표

AUC

평가 결과

이 벤치마크에서 각 모델의 성능 결과

Paper TitleRepository
DAC(STG-NF + Jigsaw)87.72%Divide and Conquer in Video Anomaly Detection: A Comprehensive Review and New Approach
MULDE-object-centric-micro86.7%MULDE: Multiscale Log-Density Estimation via Denoising Score Matching for Video Anomaly Detection
AI-VAD85.94%An Attribute-based Method for Video Anomaly Detection
STG-NF85.9%Normalizing Flows for Human Pose Anomaly Detection
AnomalyRuler85.2%Follow the Rules: Reasoning for Video Anomaly Detection with Large Language Models
VideoPatchCore85.1%VideoPatchCore: An Effective Method to Memorize Normality for Video Anomaly Detection
Jigsaw-VAD84.3%Video Anomaly Detection by Solving Decoupled Spatio-Temporal Jigsaw Puzzles
SSMTL++v283.8%SSMTL++: Revisiting Self-Supervised Multi-Task Learning for Video Anomaly Detection-
SSMTL+UBnormal83.7%UBnormal: New Benchmark for Supervised Open-Set Video Anomaly Detection
two-stream83.7%Context Recovery and Knowledge Retrieval: A Novel Two-Stream Framework for Video Anomaly Detection
Background- Agnostic Framework+SSPCAB83.6%Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detection
SSMTL+++SSMCTB83.6%Self-Supervised Masked Convolutional Transformer Block for Anomaly Detection
MoPRL83.35Regularity Learning via Explicit Distribution Modeling for Skeletal Video Anomaly Detection
SSMTL++v182.9%SSMTL++: Revisiting Self-Supervised Multi-Task Learning for Video Anomaly Detection-
Background-Agnostic Framework82.7%A Background-Agnostic Framework with Adversarial Training for Abnormal Event Detection in Video
SSMTL82.4%Anomaly Detection in Video via Self-Supervised and Multi-Task Learning
MULDE-frame-centric-micro81.3%MULDE: Multiscale Log-Density Estimation via Denoising Score Matching for Video Anomaly Detection
TSGAD80.6%An Exploratory Study on Human-Centric Video Anomaly Detection through Variational Autoencoders and Trajectory Prediction
DMAD78.8%Diversity-Measurable Anomaly Detection
Object-centric AE78.7%Object-centric Auto-encoders and Dummy Anomalies for Abnormal Event Detection in Video
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