  Video anomaly detection (VAD) is currently a challenging task due to the
complexity of anomaly as well as the lack of labor-intensive temporal
annotations. In this paper, we propose an end-to-end Global Information Guided
(GIG) anomaly detection framework for anomaly detection using the video-level
annotations (i.e., weak labels). We propose to first mine the global pattern
cues by leveraging the weak labels in a GIG module. Then we build a spatial
reasoning module to measure the relevance between vectors in spatial domain
with the global cue vectors, and select the most related feature vectors for
temporal anomaly detection. The experimental results on the CityScene challenge
demonstrate the effectiveness of our model.
