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