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English(EN) Spatio-Temporal Multi-Order Interaction for Video Recognition

VideoMogaNet 降低视频识别计算量,性能媲美 SOTA

研究人员推出了一种新颖的视频识别方法 VideoMogaNet,该方法显著降低了计算开销。该方法采用并行多阶处理,仅使用深度卷积和基于隐式门控的聚合,实现了与通道数成线性关系的时间复杂度。此外,它还结合了空间和时间维度之间的双重交互,以在不增加成本的情况下提高表示能力。VideoMogaNet 在 UCF101、HMDB51、Kinetics-400 和 Diving48 等基准测试中表现出与当前最先进方法相当或更优的性能,同时大幅降低了其时间分支的计算需求。 AI

影响 为视频识别任务提供了一种计算效率更高的方法,有可能在资源受限的设备上实现更广泛的部署。

排序理由 该集群包含一篇学术论文,详细介绍了用于视频识别的新模型和方法。 [lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

VideoMogaNet 降低视频识别计算量,性能媲美 SOTA

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该集群包含一篇学术论文,详细介绍了用于视频识别的新模型和方法。 [lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Kevin D. Nguetche, Lilian Hollard, Eric Granger, Marco Pedersoli ·

    用于视频识别的时空多阶交互

    arXiv:2611.05633v1 Announce Type: cross Abstract: Many temporal approaches for video recognition rely on explicit cross-channel aggregation projections, which have quadratic complexity in the number of channels, introducing significant computational overhead. In addition, the spa…