  Video anomaly detection (VAD) -- commonly formulated as a multiple-instance
learning problem in a weakly-supervised manner due to its labor-intensive
nature -- is a challenging problem in video surveillance where the frames of
anomaly need to be localized in an untrimmed video. In this paper, we first
propose to utilize the ViT-encoded visual features from CLIP, in contrast with
the conventional C3D or I3D features in the domain, to efficiently extract
discriminative representations in the novel technique. We then model temporal
dependencies and nominate the snippets of interest by leveraging our proposed
Temporal Self-Attention (TSA). The ablation study confirms the effectiveness of
TSA and ViT feature. The extensive experiments show that our proposed CLIP-TSA
outperforms the existing state-of-the-art (SOTA) methods by a large margin on
three commonly-used benchmark datasets in the VAD problem (UCF-Crime,
ShanghaiTech Campus, and XD-Violence). Our source code is available at
https://github.com/joos2010kj/CLIP-TSA.
