  Weakly supervised video anomaly detection aims to identify abnormal events in
videos using only video-level labels. Recently, two-stage self-training methods
have achieved significant improvements by self-generating pseudo labels and
self-refining anomaly scores with these labels. As the pseudo labels play a
crucial role, we propose an enhancement framework by exploiting completeness
and uncertainty properties for effective self-training. Specifically, we first
design a multi-head classification module (each head serves as a classifier)
with a diversity loss to maximize the distribution differences of predicted
pseudo labels across heads. This encourages the generated pseudo labels to
cover as many abnormal events as possible. We then devise an iterative
uncertainty pseudo label refinement strategy, which improves not only the
initial pseudo labels but also the updated ones obtained by the desired
classifier in the second stage. Extensive experimental results demonstrate the
proposed method performs favorably against state-of-the-art approaches on the
UCF-Crime, TAD, and XD-Violence benchmark datasets.
