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Swift Sampling 算法高效选择关键视频帧

研究人员开发了 Swift Sampling,这是一种旨在识别和选择长视频中最具信息量帧的新算法。这种无需训练的方法使用泰勒展开来预测帧演变,并选择与这些预测显著不同的帧,从而有效地捕捉时间上的意外。Swift Sampling 在计算上效率很高,与基线相比只有 0.02 倍的开销,并且在视频问答和其他下游任务中,尤其是在帧预算有限的视频上,已显示出显著的准确性提高。 AI

影响 提供了一种计算高效的方法来识别长视频中的关键时刻,有可能提高视频分析任务的性能。

排序理由 该集群包含一篇详细介绍视频帧选择新算法的学术论文。

在 arXiv cs.AI 阅读 →

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Swift Sampling 算法高效选择关键视频帧

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该集群包含一篇详细介绍视频帧选择新算法的学术论文。
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报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Deepti Ghadiyaram ·

    Swift Sampling: Selecting Temporal Surprises via Taylor Series

    While most frames in long-form video are redundant, the critical information resides in temporal surprises: moments where the actual visual features deviate from their predicted evolution. Inspired by the human brain's predictive coding, we introduce Swift Sampling, an elegant, t…

  2. arXiv cs.CV TIER_1 English(EN) · Dahye Kim, Bhuvan Sachdeva, Karan Uppal, Naman Gupta, Vineeth N. Balasubramanian, Deepti Ghadiyaram ·

    Swift Sampling: 通过泰勒级数选择时间上的惊喜

    arXiv:2605.22678v1 Announce Type: new Abstract: While most frames in long-form video are redundant, the critical information resides in temporal surprises: moments where the actual visual features deviate from their predicted evolution. Inspired by the human brain's predictive co…