研究人员推出了一种新颖的高效视频理解方法PKS$^4$，该方法解决了长视频序列的计算挑战。该方法集成了即插即用模块和线性复杂度的时域扫描，无需计算量大的注意力机制和多层适配器。PKS$^4$提取运动学先验来指导状态空间模型，实现自适应状态跟踪，与现有的视频SSM相比，训练计算量显著降低约10倍，同时在动作识别基准测试中取得了最先进的结果。 AI
影响 为高效视频理解提供了一种新范式，有望降低训练成本并提高动作识别任务的性能。
排序理由 介绍视频理解新方法的学术论文。
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arXiv:2604.26461v1 Announce Type: new Abstract: Temporal modeling remains a fundamental challenge in video understanding, particularly as sequence lengths scale. Traditional video models relying on dense spatiotemporal attention suffer from quadratic computational costs for long …
Temporal modeling remains a fundamental challenge in video understanding, particularly as sequence lengths scale. Traditional video models relying on dense spatiotemporal attention suffer from quadratic computational costs for long videos. To circumvent these costs, recent approa…