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Cells are sometimes infected by more than one virus at once. When two viruses disassemble to expose their genomes for replication in the same cell, similar regions of their genomes can pair together and exchange sequences in a process called recombination. Alternatively, viruses with segmented genomes can swap segments in a process called reassortment.
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    本研究介绍了一种自动方法,用于使用视频字幕对创建大型数据集,用于复合图像检索 (CoIR) 和复合视频检索 (CoVR). 这种方法扩展了数据集的创建,并提高了检索性能.

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    科学领域:

    • 计算机科学 计算机科学
    • 人工智能的人工智能
    • 机器学习 机器学习

    背景情况:

    • 构成图像检索 (CoIR) 是一个流行的任务,涉及图像搜索的文本和图像查询.
    • 现有的CoIR方法依赖于昂贵的,手动注释的数据集 (图像-文本-图像三重体).
    • 手动数据策划限制了CoIR方法的可扩展性.

    研究的目的:

    • 开发一个可扩展的,自动的方法来创建用于复合视频检索 (CoVR) 和CoIR的数据集.
    • 扩大检索任务的范围,包括视频数据.
    • 为训练和评估检索模型构建大型数据集.

    主要方法:

    • 利用视频标题对自动生成CoIR和CoVR的三重组.
    • 使用大型语言模型为三胞胎生成修改文本.
    • 利用 WebVid2M 和 Conceptual Captions 数据集创建大型训练数据集 (WebVid-CoVR 和 CoIR 三重组).
    • 调整了BLIP-2预训练以进行组合检索,并纳入了标题检索损失.

    主要成果:

    • 创建了WebVid-CoVR数据集,其中有160万个三胞胎和330万个CoIR培训三胞胎.
    • 引入了CoVR的新基准,并提供了基线结果.
    • 从对新数据集进行培训的 CoVR 模型向 CoIR 任务展示了有效的学习转移.
    • 在CIRR,FashionIQ和CIRCO基准上实现了在零射击检索中改进的最先进的性能.

    结论:

    • 拟议的自动数据集创建方法对于CoIR和CoVR来说是可扩展和有效的.
    • 新的数据集和基准数据促进了复合检索的研究.
    • 这种方法显著提高了检索性能,特别是在零射击设置中.