Weakly Supervised Video Anomaly Detection Based on Cross-Batch Clustering Guidance

• URL: http://arxiv.org/abs/2212.08506v1
• Date: Fri, 16 Dec 2022 14:38:30 GMT
• Title: Weakly Supervised Video Anomaly Detection Based on Cross-Batch Clustering Guidance
• Authors: Congqi Cao, Xin Zhang, Shizhou Zhang, Peng Wang, and Yanning Zhang
• Abstract summary: Weakly supervised video anomaly detection (WSVAD) is a challenging task since only video-level labels are available for training. We propose a novel WSVAD method based on cross-batch clustering guidance.
• Score: 39.43891080713327
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Weakly supervised video anomaly detection (WSVAD) is a challenging task since only video-level labels are available for training. In previous studies, the discriminative power of the learned features is not strong enough, and the data imbalance resulting from the mini-batch training strategy is ignored. To address these two issues, we propose a novel WSVAD method based on cross-batch clustering guidance. To enhance the discriminative power of features, we propose a batch clustering based loss to encourage a clustering branch to generate distinct normal and abnormal clusters based on a batch of data. Meanwhile, we design a cross-batch learning strategy by introducing clustering results from previous mini-batches to reduce the impact of data imbalance. In addition, we propose to generate more accurate segment-level anomaly scores based on batch clustering guidance further improving the performance of WSVAD. Extensive experiments on two public datasets demonstrate the effectiveness of our approach.

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