Decoupled Sensitivity-Consistency Learning for Weakly Supervised Video Anomaly Detection
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| Main Authors: | , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866918399958843392 |
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| author | Zheng, Hantao Han, Ning Zeng, Yawen Chen, Hao |
| author_facet | Zheng, Hantao Han, Ning Zeng, Yawen Chen, Hao |
| contents | Recent weakly supervised video anomaly detection methods have achieved significant advances by employing unified frameworks for joint optimization. However, this paradigm is limited by a fundamental sensitivity-stability trade-off, as the conflicting objectives for detecting transient and sustained anomalies lead to either fragmented predictions or over-smoothed responses. To address this limitation, we propose DeSC, a novel Decoupled Sensitivity-Consistency framework that trains two specialized streams using distinct optimization strategies. The temporal sensitivity stream adopts an aggressive optimization strategy to capture high-frequency abrupt changes, whereas the semantic consistency stream applies robust constraints to maintain long-term coherence and reduce noise. Their complementary strengths are fused through a collaborative inference mechanism that reduces individual biases and produces balanced predictions. Extensive experiments demonstrate that DeSC establishes new state-of-the-art performance by achieving 89.37% AUC on UCF-Crime (+1.29%) and 87.18% AP on XD-Violence (+2.22%). Code is available at https://github.com/imzht/DeSC. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2603_19780 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Decoupled Sensitivity-Consistency Learning for Weakly Supervised Video Anomaly Detection Zheng, Hantao Han, Ning Zeng, Yawen Chen, Hao Computer Vision and Pattern Recognition I.4.8; I.4.9 Recent weakly supervised video anomaly detection methods have achieved significant advances by employing unified frameworks for joint optimization. However, this paradigm is limited by a fundamental sensitivity-stability trade-off, as the conflicting objectives for detecting transient and sustained anomalies lead to either fragmented predictions or over-smoothed responses. To address this limitation, we propose DeSC, a novel Decoupled Sensitivity-Consistency framework that trains two specialized streams using distinct optimization strategies. The temporal sensitivity stream adopts an aggressive optimization strategy to capture high-frequency abrupt changes, whereas the semantic consistency stream applies robust constraints to maintain long-term coherence and reduce noise. Their complementary strengths are fused through a collaborative inference mechanism that reduces individual biases and produces balanced predictions. Extensive experiments demonstrate that DeSC establishes new state-of-the-art performance by achieving 89.37% AUC on UCF-Crime (+1.29%) and 87.18% AP on XD-Violence (+2.22%). Code is available at https://github.com/imzht/DeSC. |
| title | Decoupled Sensitivity-Consistency Learning for Weakly Supervised Video Anomaly Detection |
| topic | Computer Vision and Pattern Recognition I.4.8; I.4.9 |
| url | https://arxiv.org/abs/2603.19780 |