基于异常连通分量与意图推理的弱监督视频异常检测
Weakly Supervised Video Anomaly Detection with Anomaly-Connected Components and Intention Reasoning
把「异常连通分量」当作无需帧级标注的伪语义分组手段，配合意图感知区分相似正常/异常行为，可直接迁移到其他弱监督时序定位任务（如弱监督动作定位、视频篡改定位）中构造帧级伪标签。
原文依据 assign video frames into distinct semantic groups within a video, and frame segments within the same group are deemed to share identical semantic information
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论弱监督视频异常检测（WS-VAD）旨在未剪辑视频中定位包含异常事件的时序区间，其中仅提供视频级标注作为监督信号。然而，WS-VAD 仍存在一个关键局限：缺少稠密的帧级标注，这往往使现有方法难以有效学习异常语义。为解决该问题，我们提出一个名为 LAS-VAD 的新框架，全称为面向 WS-VAD 的异常语义学习（Learning Anomaly Semantics for WS-VAD），它集成了异常连通分量机制与意图感知机制。前者旨在将视频帧划分到视频内不同的语义组中，同一组内的帧片段被认为共享相同的语义信息。后者利用意图感知策略区分相似的正 常与异常行为（例如拿走物品与偷窃）。为进一步建模异常的语义信息，考虑到异常发生伴随显著的特征属性（即爆炸以火焰和浓烟为特征），我们额外引入异常属性信息以引导准确检测。在两个基准数据集 XD-Violence 和 UCF-Crime 上的大量实验表明，我们的 LAS-VAD 以显著优势优于当前最优方法。
英文原文摘要 1386 字符
Weakly supervised video anomaly detection (WS-VAD) involves identifying the temporal intervals that contain anomalous events in untrimmed videos, where only video-level annotations are provided as supervisory signals. However, a key limitation persists in WS-VAD, as dense frame-level annotations are absent, which often leaves existing methods struggling to learn anomaly semantics effectively. To address this issue, we propose a novel framework named LAS-VAD, short for Learning Anomaly Semantics for WS-VAD, which integrates anomaly-connected component mechanism and intention awareness mechanism. The former is designed to assign video frames into distinct semantic groups within a video, and frame segments within the same group are deemed to share identical semantic information. The latter leverages an intention-aware strategy to distinguish between similar normal and abnormal behaviors (e.g., taking items and stealing). To further model the semantic information of anomalies, as anomaly occurrence is accompanied by distinct characteristic attributes (i.e., explosions are characterized by flames and thick smoke), we additionally incorporate anomaly attribute information to guide accurate detection. Extensive experiments on two benchmark datasets, XD-Violence and UCF-Crime, demonstrate that our LAS-VAD outperforms current state-of-the-art methods with remarkable gains.