  Video anomaly detection aims to develop automated models capable of
identifying abnormal events in surveillance videos. The benchmark setup for
this task is extremely challenging due to: i) the limited size of the training
sets, ii) weak supervision provided in terms of video-level labels, and iii)
intrinsic class imbalance induced by the scarcity of abnormal events. In this
work, we show that distilling knowledge from aggregated representations of
multiple backbones into a relatively simple model achieves state-of-the-art
performance. In particular, we develop a bi-level distillation approach along
with a novel disentangled cross-attention-based feature aggregation network.
Our proposed approach, DAKD (Distilling Aggregated Knowledge with Disentangled
Attention), demonstrates superior performance compared to existing methods
across multiple benchmark datasets. Notably, we achieve significant
improvements of 1.36%, 0.78%, and 7.02% on the UCF-Crime, ShanghaiTech, and
XD-Violence datasets, respectively.
