论文标题
基于一致性的自我监督学习，用于时间异常定位
Consistency-based Self-supervised Learning for Temporal Anomaly Localization
论文作者
论文摘要
这项工作解决了弱监督的异常检测，其中允许预测变量不仅从正常示例中学习，而且还可以从训练期间提供的一些标记的异常。特别是，我们处理视频流中异常活动的本地化：这是一个非常具有挑战性的情况，因为培训示例仅带有视频级别的注释（而不是帧级）。最近的一些著作提出了各种正则化术语来解决它，即通过对弱学习的框架级异常得分的稀疏性和平滑度约束。在这项工作中，我们受到自我监督学习领域的最新进展的启发，并要求模型为同一视频序列的不同增强而产生相同的分数。我们表明，执行这样的对齐可以提高模型对XD暴力的性能。
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.
