  Weakly supervised video anomaly detection (WS-VAD) is to distinguish
anomalies from normal events based on discriminative representations. Most
existing works are limited in insufficient video representations. In this work,
we develop a multiple instance self-training framework (MIST)to efficiently
refine task-specific discriminative representations with only video-level
annotations. In particular, MIST is composed of 1) a multiple instance pseudo
label generator, which adapts a sparse continuous sampling strategy to produce
more reliable clip-level pseudo labels, and 2) a self-guided attention boosted
feature encoder that aims to automatically focus on anomalous regions in frames
while extracting task-specific representations. Moreover, we adopt a
self-training scheme to optimize both components and finally obtain a
task-specific feature encoder. Extensive experiments on two public datasets
demonstrate the efficacy of our method, and our method performs comparably to
or even better than existing supervised and weakly supervised methods,
specifically obtaining a frame-level AUC 94.83% on ShanghaiTech.
