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

Consistency-based Self-supervised Learning for Temporal Anomaly Localization