HyperAI超神经
首页
资讯
最新论文
教程
数据集
百科
SOTA
LLM 模型天梯
GPU 天梯
顶会
开源项目
全站搜索
关于
中文
HyperAI超神经
Toggle sidebar
全站搜索…
⌘
K
首页
SOTA
Anomaly Detection
Anomaly Detection On Shanghaitech
Anomaly Detection On Shanghaitech
评估指标
AUC
评测结果
各个模型在此基准测试上的表现结果
Columns
模型名称
AUC
Paper Title
Repository
AnomalyRuler
85.2%
Follow the Rules: Reasoning for Video Anomaly Detection with Large Language Models
VideoPatchCore
85.1%
VideoPatchCore: An Effective Method to Memorize Normality for Video Anomaly Detection
TSGAD
80.6%
An Exploratory Study on Human-Centric Video Anomaly Detection through Variational Autoencoders and Trajectory Prediction
PGM
61.28%
Bounding Boxes and Probabilistic Graphical Models: Video Anomaly Detection Simplified
EVAL
76.63%
EVAL: Explainable Video Anomaly Localization
-
SSMTL+UBnormal
83.7%
UBnormal: New Benchmark for Supervised Open-Set Video Anomaly Detection
MPED-RNN
73.40%
Learning Regularity in Skeleton Trajectories for Anomaly Detection in Videos
Jigsaw-VAD
84.3%
Video Anomaly Detection by Solving Decoupled Spatio-Temporal Jigsaw Puzzles
Multi-timescale Prediction
76.03%
Multi-timescale Trajectory Prediction for Abnormal Human Activity Detection
-
Any-Shot Sequential
71.6%
Any-Shot Sequential Anomaly Detection in Surveillance Videos
-
Background- Agnostic Framework+SSPCAB
83.6%
Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detection
SSMTL++v2
83.8%
SSMTL++: Revisiting Self-Supervised Multi-Task Learning for Video Anomaly Detection
-
STAN
76.2%
STAN: Spatio-Temporal Adversarial Networks for Abnormal Event Detection
-
ASTNet
73.6
Attention-based residual autoencoder for video anomaly detection
two-stream
83.7%
Context Recovery and Knowledge Retrieval: A Novel Two-Stream Framework for Video Anomaly Detection
Sparse Coding Stacked RNN
68.0%
A Revisit of Sparse Coding Based Anomaly Detection in Stacked RNN Framework
STPT
77.1%
Spatio-temporal predictive tasks for abnormal event detection in videos
-
DMAD
78.8%
Diversity-Measurable Anomaly Detection
DAC(STG-NF + Jigsaw)
87.72%
Divide and Conquer in Video Anomaly Detection: A Comprehensive Review and New Approach
MULDE-object-centric-micro
86.7%
MULDE: Multiscale Log-Density Estimation via Denoising Score Matching for Video Anomaly Detection
0 of 31 row(s) selected.
Previous
Next