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.

通过将多个骨干模型的聚合表示进行知识蒸馏，我们提出了一种相对简单的模型，达到了最先进的性能。我们的方法在多个基准数据集上取得了显著提高，尤其是在 UCF-Crime、ShanghaiTech 和 XD-Violence 数据集上，分别实现了 1.36％、0.78％和 7.02％的显著改进。