  Open Set Video Anomaly Detection (OpenVAD) aims to identify abnormal events
from video data where both known anomalies and novel ones exist in testing.
Unsupervised models learned solely from normal videos are applicable to any
testing anomalies but suffer from a high false positive rate. In contrast,
weakly supervised methods are effective in detecting known anomalies but could
fail in an open world. We develop a novel weakly supervised method for the
OpenVAD problem by integrating evidential deep learning (EDL) and normalizing
flows (NFs) into a multiple instance learning (MIL) framework. Specifically, we
propose to use graph neural networks and triplet loss to learn discriminative
features for training the EDL classifier, where the EDL is capable of
identifying the unknown anomalies by quantifying the uncertainty. Moreover, we
develop an uncertainty-aware selection strategy to obtain clean anomaly
instances and a NFs module to generate the pseudo anomalies. Our method is
superior to existing approaches by inheriting the advantages of both the
unsupervised NFs and the weakly-supervised MIL framework. Experimental results
on multiple real-world video datasets show the effectiveness of our method.
