Abstract
Video anomaly detection under weak labels is formulated as a typical multiple-instance learning problem in previous works. In this paper, we provide a new perspective, i.e., a supervised learning task under noisy labels. In such a viewpoint, as long as cleaning away label noise, we can directly apply fully supervised action classifiers to weakly supervised anomaly detection, and take maximum advantage of these well-developed classifiers. For this purpose, we devise a graph convolutional network to correct noisy labels. Based upon feature similarity and temporal consistency, our network propagates supervisory signals from high-confidence snippets to low-confidence ones. In this manner, the network is capable of providing cleaned supervision for action classifiers. During the test phase, we only need to obtain snippet-wise predictions from the action classifier without any extra post-processing. Extensive experiments on 3 datasets at different scales with 2 types of action classifiers demonstrate the efficacy of our method. Remarkably, we obtain the frame-level AUC score of 82.12% on UCF-Crime.
Abstract (translated)
弱标签下的视频异常检测是前人研究的一个典型的多实例学习问题。在本文中，我们提供了一个新的视角，即在嘈杂标签下的监督学习任务。在这种观点下，只要清除标签噪声，就可以直接将全监督行为分类器应用于弱监督异常检测，并充分利用这些发达的分类器。为此，我们设计了一个图卷积网络来校正噪声标签。基于特征相似性和时间一致性，我们的网络将监控信号从高置信片段传播到低置信片段。通过这种方式，网络能够为动作分类器提供干净的监控。在测试阶段，我们只需要从动作分类器中获得逐段的预测，而不需要任何额外的后处理。通过对3个不同尺度的数据集和2种类型的动作分类器的大量实验，证明了该方法的有效性。值得注意的是，我们得出了UCF犯罪的帧级AUC得分为82.12%。
URL
https://arxiv.org/abs/1903.07256
