Papers › Self-Distilled Masked Auto-Encoders are Efficient Video Anomaly Detectors
Self-Distilled Masked Auto-Encoders are Efficient Video Anomaly Detectors
Nicolae-Catalin Ristea, Florinel-Alin Croitoru, Radu Tudor Ionescu, Marius Popescu, Fahad Shahbaz Khan, Mubarak Shah
We propose an efficient abnormal event detection model based on a lightweight masked auto-encoder (AE) applied at the video frame level. The novelty of the proposed model is threefold. First, we introduce an approach to weight tokens based on motion gradients, thus shifting the focus from the static background scene to the foreground objects. Second, we integrate a teacher decoder and a student decoder into our architecture, leveraging the discrepancy between the outputs given by the two decoders to improve anomaly detection. Third, we generate synthetic abnormal events to augment the training videos, and task the masked AE model to jointly reconstruct the original frames (without anomalies) and the corresponding pixel-level anomaly maps. Our design leads to an efficient and effective model, as demonstrated by the extensive experiments carried out on four benchmarks: Avenue, ShanghaiTech, UBnormal and UCSD Ped2. The empirical results show that our model achieves an excellent trade-off between speed and accuracy, obtaining competitive AUC scores, while processing 1655 FPS. Hence, our model is between 8 and 70 times faster than competing methods. We also conduct an ablation study to justify our design. Our code is freely available at: https://github.com/ristea/aed-mae.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Anomaly Detection | CUHK Avenue | SD-MAE | AUC | 91.3% | #14 of 35 | Archive leaderboard | report |
| Anomaly Detection | CUHK Avenue | SD-MAE | FPS | 1670 | #14 of 35 | Archive leaderboard | report |
| Anomaly Detection | CUHK Avenue | SD-MAE | RBDC | 46.77 | #14 of 35 | Archive leaderboard | report |
| Anomaly Detection | CUHK Avenue | SD-MAE | TBDC | 66.58 | #14 of 35 | Archive leaderboard | report |
| Anomaly Detection | UCSD Ped2 | SD-MAE | AUC | 95.4% | #13 of 14 | Archive leaderboard | report |
| Anomaly Detection | UCSD Ped2 | SD-MAE | FPS | 1655 | #13 of 14 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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