Anomaly Detection In Surveillance Videos On 2

Results

Performance results of various models on this benchmark

Model Name	AP	Paper Title	Repository
MSBT	84.32	Multi-scale Bottleneck Transformer for Weakly Supervised Multimodal Violence Detection	-
CSL_TAL	71.68	Consistency-based Self-supervised Learning for Temporal Anomaly Localization
CFA-HLGAtt	86.34	Cross-Modal Fusion and Attention Mechanism for Weakly Supervised Video Anomaly Detection	-
MACIL_SD	83.4	Modality-Aware Contrastive Instance Learning with Self-Distillation for Weakly-Supervised Audio-Visual Violence Detection
S3R (without audio imformation)	80.26	Self-supervised Sparse Representation for Video Anomaly Detection
HyperVD	85.67	Learning Weakly Supervised Audio-Visual Violence Detection in Hyperbolic Space
Contrastive Attention for Video Anomaly Detection	76.9	-	-
PEL	85.59	Learning Prompt-Enhanced Context Features for Weakly-Supervised Video Anomaly Detection
MGFN	80.11	MGFN: Magnitude-Contrastive Glance-and-Focus Network for Weakly-Supervised Video Anomaly Detection
CMA_LA	83.54	Audio-Guided Attention Network for Weakly Supervised Violence Detection
MAVD	86.07	Aligning First, Then Fusing: A Novel Weakly Supervised Multimodal Violence Detection Method
RTFM	77.81	Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learning
A Neural Network Containing Three Parallel Branches (holistic, localized, and score branch)	78.64	Not only Look, but also Listen: Learning Multimodal Violence Detection under Weak Supervision
BN-WVAD	84.93	BatchNorm-based Weakly Supervised Video Anomaly Detection

0 of 14 row(s) selected.