用交叉伪标签让弱监督视频异常检测更准，还能分清异常类型。
Cross Pseudo Labeling For Weakly Supervised Video Anomaly Detection
- 双分支结构互传伪标签，一个专注定位异常片段，一个识别异常类别。
- 在XD-Violence和UCF-Crime数据集上均达到当前最优性能。
- 适合需要同时做异常检测与分类的视频分析场景。
弱监督视频异常检测旨在仅用视频级标签下检测异常并识别异常类别。我们提出CPL-VAD，一种具有交叉伪标签的双分支框架。二分类异常检测分支专注于片段级异常定位，类别分类分支利用视觉-语言对齐来识别异常事件类别。通过伪标签交换，两个分支实现互补优势，融合时间精度与语义区分能力。在XD-Violence和UCF-Crime数据集上的实验表明，CPL-VAD在异常检测和异常类别分类上均达到当前最优性能。
原文摘要 · Abstract (English)
Weakly supervised video anomaly detection aims to detect anomalies and identify abnormal categories with only video-level labels. We propose CPL-VAD, a dual-branch framework with cross pseudo labeling. The binary anomaly detection branch focuses on snippet-level anomaly localization, while the category classification branch leverages vision-language alignment to recognize abnormal event categories. By exchanging pseudo labels, the two branches transfer complementary strengths, combining temporal precision with semantic discrimination. Experiments on XD-Violence and UCF-Crime demonstrate that CPL-VAD achieves state-of-the-art performance in both anomaly detection and abnormal category classification.
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