Papers › Weakly Supervised Video Anomaly Detection via Center-guided Discriminative Learning
Weakly Supervised Video Anomaly Detection via Center-guided Discriminative Learning
Boyang Wan, Yuming Fang, Xue Xia, Jiajie Mei
Anomaly detection in surveillance videos is a challenging task due to the diversity of anomalous video content and duration. In this paper, we consider video anomaly detection as a regression problem with respect to anomaly scores of video clips under weak supervision. Hence, we propose an anomaly detection framework, called Anomaly Regression Net (AR-Net), which only requires video-level labels in training stage. Further, to learn discriminative features for anomaly detection, we design a dynamic multiple-instance learning loss and a center loss for the proposed AR-Net. The former is used to enlarge the inter-class distance between anomalous and normal instances, while the latter is proposed to reduce the intra-class distance of normal instances. Comprehensive experiments are performed on a challenging benchmark: ShanghaiTech. Our method yields a new state-of-the-art result for video anomaly detection on ShanghaiTech dataset
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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 | AR-Net | AUC-ROC | 91.24 | #10 of 12 | Archive leaderboard | report |
| Weakly-supervised Video Anomaly Detection | ShanghaiTech Weakly Supervised | AR-Net | AUC-ROC | 91.24 | #15 of 16 | Archive leaderboard | report |
| Weakly-supervised Video Anomaly Detection | ShanghaiTech Weakly Supervised | AR-Net | FAR-Normal | 0.10 | #15 of 16 | Archive leaderboard | report |
| Weakly-supervised Video Anomaly Detection | UBnormal | AR-Net | AUC-ROC | 62.30 | #9 of 11 | 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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