Distilling Aggregated Knowledge for Weakly-Supervised Video Anomaly Detection

• URL: http://arxiv.org/abs/2406.02831v2
• Date: Sat, 18 Jan 2025 14:34:55 GMT
• Title: Distilling Aggregated Knowledge for Weakly-Supervised Video Anomaly Detection
• Authors: Jash Dalvi, Ali Dabouei, Gunjan Dhanuka, Min Xu,
• Abstract summary: Video anomaly detection aims to develop automated models capable of identifying abnormal events in surveillance videos. We show that distilling knowledge from aggregated representations of multiple backbones into a single-backbone Student model achieves state-of-the-art performance.
• Score: 11.250490586786878
• License:
• Abstract: Video anomaly detection aims to develop automated models capable of identifying abnormal events in surveillance videos. The benchmark setup for this task is extremely challenging due to: i) the limited size of the training sets, ii) weak supervision provided in terms of video-level labels, and iii) intrinsic class imbalance induced by the scarcity of abnormal events. In this work, we show that distilling knowledge from aggregated representations of multiple backbones into a single-backbone Student model achieves state-of-the-art performance. In particular, we develop a bi-level distillation approach along with a novel disentangled cross-attention-based feature aggregation network. Our proposed approach, DAKD (Distilling Aggregated Knowledge with Disentangled Attention), demonstrates superior performance compared to existing methods across multiple benchmark datasets. Notably, we achieve significant improvements of 1.36%, 0.78%, and 7.02% on the UCF-Crime, ShanghaiTech, and XD-Violence datasets, respectively.

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