| STEAD-Base |
- ROC AUC
- 91.34
|
STEAD: Spatio-Temporal Efficient Anomaly Detection for Time and Compute Sensitive Applications 2025-03-11 |
코드 1 |
| MTFL (VST, finetuned on VADD) |
- ROC AUC
- 89.78
|
MTFL: Multi-Timescale Feature Learning for Weakly-Supervised Anomaly Detection in Surveillance Videos 2024-10-08 |
코드 1 |
| STEAD-Fast |
- ROC AUC
- 88.87
|
STEAD: Spatio-Temporal Efficient Anomaly Detection for Time and Compute Sensitive Applications 2025-03-11 |
코드 1 |
| BN-WVAD |
- ROC AUC
- 87.24
|
BatchNorm-based Weakly Supervised Video Anomaly Detection 2023-11-26 |
코드 1 |
| MTFL (VST) |
- ROC AUC
- 87.16
|
MTFL: Multi-Timescale Feature Learning for Weakly-Supervised Anomaly Detection in Surveillance Videos 2024-10-08 |
코드 1 |
| ProDisc-VAD |
- ROC AUC
- 87.12
|
ProDisc-VAD: An Efficient System for Weakly-Supervised Anomaly Detection in Video Surveillance Applications 2025-05-04 |
코드 1 |
| MGFN |
- ROC AUC
- 86.98
|
MGFN: Magnitude-Contrastive Glance-and-Focus Network for Weakly-Supervised Video Anomaly Detection 2022-11-28 |
코드 1 |
| PEL |
- ROC AUC
- 86.76
|
Learning Prompt-Enhanced Context Features for Weakly-Supervised Video Anomaly Detection 2023-06-26 |
코드 1 |
| S3R |
- ROC AUC
- 85.99
|
Self-supervised Sparse Representation for Video Anomaly Detection 2022-10-23 |
코드 1 |
| WSAL |
- ROC AUC
- 85.38
|
Localizing Anomalies from Weakly-Labeled Videos 2020-08-20 |
코드 1 |
| CR-UNet |
- ROC AUC
- 85.24
|
Contrastive-Regularized U-Net for Video Anomaly Detection 2023-04-11 |
연결 없음 |
| Multi-stream Network with Late Fuzzy Fusion |
- ROC AUC
- 84.48
|
A multi-stream deep neural network with late fuzzy fusion for real-world anomaly detection 2022-03-27 |
연결 없음 |
| RTFM |
- ROC AUC
- 84.03
|
Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learning 2021-01-25 |
코드 1 · 코드 2 · 코드 3 |
| MIST |
- ROC AUC
- 82.30
|
MIST: Multiple Instance Self-Training Framework for Video Anomaly Detection 2021-04-04 |
코드 1 |
| DMRMs |
- ROC AUC
- 81.91
- Decidability
- -
- EER
- -
|
Anomalous Event Recognition in Videos Based on Joint Learningof Motion and Appearance with Multiple Ranking Measures 2021-02-02 |
연결 없음 |
| Multiple-Instance-Based-Video-Anomaly-Detection-Using-Deep-Temporal-Encoding-Decoding |
- ROC AUC
- 80.10
- Decidability
- -
- EER
- -
|
Multiple Instance-Based Video Anomaly Detection using Deep Temporal Encoding-Decoding 2020-07-03 |
코드 1 |
| MULDE-frame-centric-micro-one-class-classification |
- ROC AUC
- 78.5%
|
MULDE: Multiscale Log-Density Estimation via Denoising Score Matching for Video Anomaly Detection 2024-03-21 |
코드 1 |
| MILR |
- ROC AUC
- 76.67
- Decidability
- -
- EER
- -
|
3D ResNet with Ranking Loss Function for Abnormal Activity Detection in Videos 2020-02-04 |
연결 없음 |
| GMM-based |
- ROC AUC
- 75.90
- Decidability
- 0.885
- EER
- 0.302
|
Weakly and Partially Supervised Learning Frameworks for Anomaly Detection 2020-07-23 |
코드 1 |
| Sultani et al. |
- ROC AUC
- 75.41
- Decidability
- 0.613
- EER
- 0.353
|
Real-world Anomaly Detection in Surveillance Videos 2018-01-12 |
코드 1 · 코드 2 · 코드 3 · 코드 4 · 코드 5 · 코드 6 · 코드 7 · 코드 8 · 코드 9 |
| DAKD (Weakly-supervised) |
- AUC
- 88.34
|
Distilling Aggregated Knowledge for Weakly-Supervised Video Anomaly Detection 2024-06-05 |
연결 없음 |