Weakly-Supervised Video Anomaly Detection with Snippet Anomalous
  Attention
Yidan Fan Yongxin Yu Wenhuan Lu Yahong Han
With a focus on abnormal events contained within untrimmed videos, there is
increasing interest among researchers in video anomaly detection. Among
different video anomaly detection scenarios, weakly-supervised video anomaly
detection poses a significant challenge as it lacks frame-wise labels during
the training stage, only relying on video-level labels as coarse supervision.
Previous methods have made attempts to either learn discriminative features in
an end-to-end manner or employ a twostage self-training strategy to generate
snippet-level pseudo labels. However, both approaches have certain limitations.
The former tends to overlook informative features at the snippet level, while
the latter can be susceptible to noises. In this paper, we propose an Anomalous
Attention mechanism for weakly-supervised anomaly detection to tackle the
aforementioned problems. Our approach takes into account snippet-level encoded
features without the supervision of pseudo labels. Specifically, our approach
first generates snippet-level anomalous attention and then feeds it together
with original anomaly scores into a Multi-branch Supervision Module. The module
learns different areas of the video, including areas that are challenging to
detect, and also assists the attention optimization. Experiments on benchmark
datasets XDViolence and UCF-Crime verify the effectiveness of our method.
Besides, thanks to the proposed snippet-level attention, we obtain a more
precise anomaly localization.