Abstract
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 this https URL.
Abstract (translated)
近期弱监督视频异常检测方法通过采用统一框架进行联合优化取得了显著进展。然而，该范式受限于根本性的敏感性-稳定性权衡问题：检测瞬态异常与持续异常的目标相互冲突，导致预测结果碎片化或响应过度平滑。为克服这一局限，我们提出DeSC——一种新颖的解耦敏感性-一致性框架，该框架采用不同的优化策略训练两个专用流。时序敏感性流采用激进优化策略以捕捉高频突变，而语义一致性流则施加稳健约束以维持长期连贯性并降低噪声。两个流通过协同推理机制融合其互补优势，该机制能减少个体偏差并产生均衡预测。大量实验表明，DeSC在UCF-Crime上达到89.37%的AUC（+1.29%），在XD-Violence上达到87.18%的AP（+2.22%），建立了新的最先进性能。代码已公开于此https链接。
URL
https://arxiv.org/abs/2603.19780
