  Weakly supervised detection of anomalies in surveillance videos is a
challenging task. Going beyond existing works that have deficient capabilities
to localize anomalies in long videos, we propose a novel glance and focus
network to effectively integrate spatial-temporal information for accurate
anomaly detection. In addition, we empirically found that existing approaches
that use feature magnitudes to represent the degree of anomalies typically
ignore the effects of scene variations, and hence result in sub-optimal
performance due to the inconsistency of feature magnitudes across scenes. To
address this issue, we propose the Feature Amplification Mechanism and a
Magnitude Contrastive Loss to enhance the discriminativeness of feature
magnitudes for detecting anomalies. Experimental results on two large-scale
benchmarks UCF-Crime and XD-Violence manifest that our method outperforms
state-of-the-art approaches.
