  We present a meta-learning framework for weakly supervised anomaly detection
in videos, where the detector learns to adapt to unseen types of abnormal
activities effectively when only video-level annotations of binary labels are
available. Our work is motivated by the fact that existing methods suffer from
poor generalization to diverse unseen examples. We claim that an anomaly
detector equipped with a meta-learning scheme alleviates the limitation by
leading the model to an initialization point for better optimization. We
evaluate the performance of our framework on two challenging datasets,
UCF-Crime and ShanghaiTech. The experimental results demonstrate that our
algorithm boosts the capability to localize unseen abnormal events in a weakly
supervised setting. Besides the technical contributions, we perform the
annotation of missing labels in the UCF-Crime dataset and make our task
evaluated effectively.
