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Video Abnormal Event Detection by Learning to Complete Visual Cloze Tests
[article]
2021
arXiv
pre-print
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
arXiv:2108.02356v2
fatcat:7sl2musf7vecrdtdhwcqb2nsjy