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English(EN) DUGO: Dual-Scale Gaussian Memory for Online Temporal Action Localization in Egocentric Procedural Videos

DUGO框架增强了单目视频中的在线动作定位

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员推出了一种新颖的单目视频在线时序动作定位框架DUGO。DUGO通过联合建模局部运动动力学和长程时序依赖性来解决现有方法的局限性。它采用双尺度时序编码器来处理细粒度模式和更广泛的上下文，并结合高斯记忆库，将动作记忆表示为随机高斯原型，以实现更丰富的时序回忆。在EGTEA和EPIC-Kitchens-100等数据集上的实验表明，DUGO取得了最先进的性能。 AI

影响 增强了实时视频分析能力，可能改进机器人、监控和内容审核等应用。

排序理由 该条目是一篇学术论文，详细介绍了一种新的时序动作定位方法。

在 arXiv cs.LG 阅读 →

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

DUGO框架增强了单目视频中的在线动作定位

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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Mohammed Aman Bhuiyan, Md. Sazzad Hossain Adib, Samiul Basir Bhuiyan, Mir Faiyaz Hossain, Syed Mohammed Shamsul Islam, Md Zakir Hossain, Shafin Rahman ·

    DUGO: Dual-Scale Gaussian Memory for Online Temporal Action Localization in Egocentric Procedural Videos

    arXiv:2610.41402v1 Announce Type: cross Abstract: Online Temporal Action Localization (OnTAL) aims to detect and localize action instances from untrimmed streaming videos in real time. Unlike offline approaches, OnTAL models have no access to future frames and cannot go back and …

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