Papers › Self-supervised Sparse Representation for Video Anomaly Detection
Self-supervised Sparse Representation for Video Anomaly Detection
Jhih-Ciang Wu*, He-Yen Hsieh*, Ding-Jie Chen, Chiou-Shann Fuh, Tyng-Luh Liu
Video anomaly detection (VAD) aims at localizing unexpected actions or activities in a video sequence. Existing mainstream VAD techniques are based on either the one-class formulation, which assumes all training data are normal, or weakly-supervised, which requires only video-level normal/anomaly labels. To establish a unified approach to solving the two VAD settings, we introduce a self-supervised sparse representation (S3R) framework that models the concept of anomaly at feature level by exploring the synergy between dictionary-based representation and self-supervised learning. With the learned dictionary, S3R facilitates two coupled modules, en-Normal and de-Normal, to reconstruct snippet-level features and filter out normal-event features. The self-supervised techniques also enable generating samples of pseudo normal/anomaly to train the anomaly detector. We demonstrate with extensive experiments that S3R achieves new state-of-the-art performances on popular benchmark datasets for both one-class and weakly-supervised VAD tasks. Our code is publicly available at https://github.com/louisYen/S3R.
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 In Surveillance Videos | ShanghaiTech Weakly Supervised | S3R | AUC-ROC | 97.48 | #4 of 12 | Archive leaderboard | report |
| Anomaly Detection In Surveillance Videos | UCF-Crime | S3R | ROC AUC | 85.99 | #9 of 21 | Archive leaderboard | report |
| Anomaly Detection In Surveillance Videos | XD-Violence | S3R (without audio imformation) | AP | 80.26 | #11 of 17 | Archive leaderboard | report |
| Weakly-supervised Video Anomaly Detection | ShanghaiTech Weakly Supervised | S3R | AUC-ROC | 97.48 | #8 of 16 | 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.
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