  In weakly supervised video anomaly detection (WVAD), where only video-level
labels indicating the presence or absence of abnormal events are available, the
primary challenge arises from the inherent ambiguity in temporal annotations of
abnormal occurrences. Inspired by the statistical insight that temporal
features of abnormal events often exhibit outlier characteristics, we propose a
novel method, BN-WVAD, which incorporates BatchNorm into WVAD. In the proposed
BN-WVAD, we leverage the Divergence of Feature from Mean vector (DFM) of
BatchNorm as a reliable abnormality criterion to discern potential abnormal
snippets in abnormal videos. The proposed DFM criterion is also discriminative
for anomaly recognition and more resilient to label noise, serving as the
additional anomaly score to amend the prediction of the anomaly classifier that
is susceptible to noisy labels. Moreover, a batch-level selection strategy is
devised to filter more abnormal snippets in videos where more abnormal events
occur. The proposed BN-WVAD model demonstrates state-of-the-art performance on
UCF-Crime with an AUC of 87.24%, and XD-Violence, where AP reaches up to
84.93%. Our code implementation is accessible at
https://github.com/cool-xuan/BN-WVAD.
