New MS2CAST framework enhances video anomaly detection with cross-attention and Vision Mamba

Researchers have developed MS2CAST, a new framework for detecting anomalies in video footage. This system utilizes a multi-scale cross-attention encoder that combines features from a vision transformer (MViTv2-S) and a CNN (ConvNeXt-T) to better capture spatial information. For modeling temporal dynamics, MS2CAST integrates either a Swin Transformer-based module (SwinLSTM) or a Vision Mamba-based module (VMRNN), avoiding the need for computationally intensive optical flow calculations. The framework has demonstrated competitive performance on standard datasets like UCSD Ped2, CUHK Avenue, and ShanghaiTech, while maintaining low GPU memory usage. AI

IMPACT Introduces a novel framework for video anomaly detection, potentially improving surveillance systems with efficient motion analysis.

RANK_REASON The item describes a new research paper detailing a novel framework for video anomaly detection. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

• arXiv
• ConvNeXt-T
• CUHK Avenue
• MS2CAST
• MViTv2-S
• ShanghaiTech
• SwinLSTM
• Swin Transformer
• UCSD Ped2
• Vision Mamba
• VMRNN

• paper
• other

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