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English(EN) MS2CAST: A Video Anomaly Detection Framework via Multi-Scale Spatial Cross-Attention and Efficient Spatiotemporal Networks

新的MS2CAST框架通过交叉注意力和Vision Mamba增强视频异常检测

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了MS2CAST，一个用于检测视频片段中异常的新框架。该系统利用多尺度交叉注意力编码器，该编码器结合了来自视觉Transformer (MViTv2-S) 和CNN (ConvNeXt-T) 的特征，以更好地捕捉空间信息。为了模拟时间动态，MS2CAST集成了基于Swin Transformer的模块 (SwinLSTM) 或基于Vision Mamba的模块 (VMRNN)，避免了计算密集的光流计算。该框架在UCSD Ped2、CUHK Avenue和ShanghaiTech等标准数据集上展示了具有竞争力的性能，同时保持了较低的GPU内存使用量。 AI

影响 引入了一个新颖的视频异常检测框架，通过高效的运动分析有可能改进监控系统。

排序理由 该条目描述了一篇详细介绍新颖视频异常检测框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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新的MS2CAST框架通过交叉注意力和Vision Mamba增强视频异常检测

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该条目描述了一篇详细介绍新颖视频异常检测框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CV TIER_1 English(EN) · Ali Safaei, Mohammad Kazemi ·

    MS2CAST: A Video Anomaly Detection Framework via Multi-Scale Spatial Cross-Attention and Efficient Spatiotemporal Networks

    arXiv:2610.29649v1 Announce Type: new Abstract: Video anomaly detection (VAD) is a challenging task for surveillance systems due to the scarcity and diversity of anomalous events. While semi-supervised VAD approaches have advanced the field, many rely on computationally intensive…

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