分离敏感性与一致性训练，提升弱监督异常检测精度
Decoupled Sensitivity-Consistency Learning for Weakly Supervised Video Anomaly Detection
- 分设敏感性与一致性双流，分别捕捉突发变化和长期一致
- 在UCF-Crime达89.37% AUC，XD-Violence达87.18% AP
- 适合需要高精度视频异常检测的工业场景
近期弱监督视频异常检测方法通过统一框架联合优化取得显著进展。然而，该范式受限于敏感性-稳定性权衡：检测瞬时与持续异常的目标冲突，导致预测碎片化或过度平滑。为此，本文提出DeSC框架，通过解耦训练两个专用分支实现突破。时间敏感流采用激进优化策略捕捉高频突变，语义一致流施加鲁棒约束以维持长期连贯性并抑制噪声。二者通过协同推理机制融合，降低个体偏差，生成平衡预测。大量实验表明，DeSC在UCF-Crime上达到89.37% AUC（+1.29%），在XD-Violence上达到87.18% AP（+2.22%），刷新当前最佳性能。代码已公开于https://github.com/imzht/DeSC。
原文摘要 · Abstract (English)
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.
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