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UBnormal: New Benchmark for Supervised Open-Set Video Anomaly Detection
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add UBnormal training anomalies to the pseudo-abnormal pool (ablation) | Georgescu anomaly detector · UBnormal test | micro AUC: 59.3 -> 61.3 | Helped |
| Fine-tune the pre-trained model on UBnormal (ablation) | Sultani · UBnormal test | micro AUC: 49.5 -> 50.3 | Helped |
| Use 1/8 or 1/4 rather than 1/32 video sample rate (ablation) | TimeSformer · UBnormal test | micro AUC: 68.5 -> 64.1; 68.5 -> 61.9 | Hurt |
| Add the UBnormal normal-versus-abnormal proxy task T5 (ablation) | Georgescu multi-task detector · Avenue and ShanghaiTech | micro AUC: Avenue: 91.5 -> 91.9; ShanghaiTech: 82.4 -> 83.0 | Helped |
| Translate UBnormal objects with CycleGAN in addition to T5 (ablation) | Georgescu multi-task detector · Avenue and ShanghaiTech | micro AUC: Avenue: 91.9 -> 93.0; ShanghaiTech: 83.0 -> 83.7 | Helped |
Closest papers by meaning
- Real-World Anomaly Detection in Surveillance Videos (CVPR 2018 · similarity 0.89)
- Generalizing Single-Frame Supervision to Event-Level Understanding for Video Anomaly Detection (NeurIPS 2025 · similarity 0.89)
- Open-Vocabulary Video Anomaly Detection (CVPR 2024 · similarity 0.88)
- Learning Event Completeness for Weakly Supervised Video Anomaly Detection (ICML 2025 · similarity 0.88)
- Holmes-VAU: Towards Long-term Video Anomaly Understanding at Any Granularity (CVPR 2025 · similarity 0.88)
- VADTree: Explainable Training-Free Video Anomaly Detection via Hierarchical Granularity-Aware Tree (NeurIPS 2025 · similarity 0.87)
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