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English(EN) VEDJE: Video-Efficient Discriminative Joint Encoder for Scalable Video-Text Retrieval

VEDJE系统通过紧凑缓存实现高效视频-文本检索

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了VEDJE，一种可扩展视频-文本检索的新型系统，可显著降低计算成本。VEDJE通过将视频特征压缩成紧凑、可重用的缓存来实现这一点，从而能够与新查询进行高效匹配。这种方法在MSR-VTT、MSVD、DiDeMo和ActivityNet等基准测试中提高了检索准确性，同时大幅缩小了每个视频的数据占用空间。 AI

影响 通过降低计算负载和数据存储要求，实现更高效、可扩展的多模态搜索系统。

排序理由 该条目描述了一篇关于视频-文本检索新方法的最新研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 Hugging Face Daily Papers 阅读 →

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

VEDJE系统通过紧凑缓存实现高效视频-文本检索

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该条目描述了一篇关于视频-文本检索新方法的最新研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    VEDJE：视频高效判别联合编码器，用于可扩展视频-文本检索

    Finding the right video often requires distinguishing similar scenes in which different events occur. Joint matching improves retrieval, but processing rich video representations for each query is costly. VEDJE compresses features within sampled frames while keeping their represe…

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