  Current weakly supervised video anomaly detection algorithms mostly use
multiple instance learning (MIL) or their varieties. Almost all recent
approaches focus on how to select the correct snippets for training to improve
the performance. They overlook or do not realize the power of video
classification in boosting the performance of anomaly detection. In this paper,
we study explicitly the power of video classification supervision using a BERT
or LSTM. With this BERT or LSTM, CNN features of all snippets of a video can be
aggregated into a single feature which can be used for video classification.
This simple yet powerful video classification supervision, combined into the
MIL framework, brings extraordinary performance improvement on all three major
video anomaly detection datasets. Particularly it improves the mean average
precision (mAP) on the XD-Violence from SOTA 78.84\% to new 82.10\%. The source
code is available at
https://github.com/wjtan99/BERT_Anomaly_Video_Classification.
