Contrastive-regularized U-Net for video anomaly detection
Video anomaly detection aims to identify anomalous segments in a video. It is typically trained with weakly supervised video-level labels. This paper focuses on two crucial factors affecting the performance of video anomaly detection models. First, we explore how to capture the local and global temp...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Published: |
Institute of Electrical and Electronics Engineers
2023
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| Subjects: | |
| Online Access: | http://eprints.um.edu.my/39002/ |
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