This work tackles Weakly Supervised Anomaly detection, in which a predictor
is allowed to learn not only from normal examples but also from a few labeled
anomalies made available during training. In particular, we deal with the
localization of anomalous activities within the video stream: this is a very
challenging scenario, as training examples come only with video-level
annotations (and not frame-level). Several recent works have proposed various
regularization terms to address it i.e. by enforcing sparsity and smoothness
constraints over the weakly-learned frame-level anomaly scores. In this work,
we get inspired by recent advances within the field of self-supervised learning
and ask the model to yield the same scores for different augmentations of the
same video sequence. We show that enforcing such an alignment improves the
performance of the model on XD-Violence.

本文针对弱监督异常检测进行研究，利用自监督学习策略，通过对同一视频序列数据进行不同变换来使模型获得相同的分数，并通过实验证明这种对齐方式可以提高模型在 XD-Violence 数据集上的性能表现。