  We propose a lightweight and accurate method for detecting anomalies in
videos. Existing methods used multiple-instance learning (MIL) to determine the
normal/abnormal status of each segment of the video. Recent successful
researches argue that it is important to learn the temporal relationships among
segments to achieve high accuracy, instead of focusing on only a single
segment. Therefore we analyzed the existing methods that have been successful
in recent years, and found that while it is indeed important to learn all
segments together, the temporal orders among them are irrelevant to achieving
high accuracy. Based on this finding, we do not use the MIL framework, but
instead propose a lightweight model with a self-attention mechanism to
automatically extract features that are important for determining
normal/abnormal from all input segments. As a result, our neural network model
has 1.3\% of the number of parameters of the existing method. We evaluated the
frame-level detection accuracy of our method on three benchmark datasets
(UCF-Crime, ShanghaiTech, and XD-Violence) and demonstrate that our method can
achieve the comparable or better accuracy than state-of-the-art methods.
