  Weakly supervised video anomaly detection (WSVAD) is a challenging task since
only video-level labels are available for training. In previous studies, the
discriminative power of the learned features is not strong enough, and the data
imbalance resulting from the mini-batch training strategy is ignored. To
address these two issues, we propose a novel WSVAD method based on cross-batch
clustering guidance. To enhance the discriminative power of features, we
propose a batch clustering based loss to encourage a clustering branch to
generate distinct normal and abnormal clusters based on a batch of data.
Meanwhile, we design a cross-batch learning strategy by introducing clustering
results from previous mini-batches to reduce the impact of data imbalance. In
addition, we propose to generate more accurate segment-level anomaly scores
based on batch clustering guidance further improving the performance of WSVAD.
Extensive experiments on two public datasets demonstrate the effectiveness of
our approach.
