  Although deep neural networks (DNNs) enable great progress in video abnormal
event detection (VAD), existing solutions typically suffer from two issues: (1)
The localization of video events cannot be both precious and comprehensive. (2)
The semantics and temporal context are under-explored. To tackle those issues,
we are motivated by the prevalent cloze test in education and propose a novel
approach named Visual Cloze Completion (VCC), which conducts VAD by learning to
complete "visual cloze tests" (VCTs). Specifically, VCC first localizes each
video event and encloses it into a spatio-temporal cube (STC). To achieve both
precise and comprehensive localization, appearance and motion are used as
complementary cues to mark the object region associated with each event. For
each marked region, a normalized patch sequence is extracted from current and
adjacent frames and stacked into a STC. With each patch and the patch sequence
of a STC compared to a visual "word" and "sentence" respectively, we
deliberately erase a certain "word" (patch) to yield a VCT. Then, the VCT is
completed by training DNNs to infer the erased patch and its optical flow via
video semantics. Meanwhile, VCC fully exploits temporal context by
alternatively erasing each patch in temporal context and creating multiple
VCTs. Furthermore, we propose localization-level, event-level, model-level and
decision-level solutions to enhance VCC, which can further exploit VCC's
potential and produce significant performance improvement gain. Extensive
experiments demonstrate that VCC achieves state-of-the-art VAD performance. Our
codes and results are open at https://github.com/yuguangnudt/VEC_VAD/tree/VCC.
