DAMS, a dual-branch architecture fusing adaptive temporal pyramids, CBAM attention, and CLIP pseudo-labels, reports 94.67 AUC on UCF-Crime and 84.00 AP on XD-Violence for weakly supervised video anomaly detection.
Memoryout: Learning principal features via multimodal sparse filtering network for semi-supervised video anomaly detection.arXiv preprint arXiv:2506.02535, 2025
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DAMS:Dual-Branch Adaptive Multiscale Spatiotemporal Framework for Video Anomaly Detection
DAMS, a dual-branch architecture fusing adaptive temporal pyramids, CBAM attention, and CLIP pseudo-labels, reports 94.67 AUC on UCF-Crime and 84.00 AP on XD-Violence for weakly supervised video anomaly detection.