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Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
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通过CLIP驱动的细粒度文本图像人重新识别.

Shuanglin Yan, Neng Dong, Liyan Zhang

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
    |November 1, 2023
    PubMed
    概括

    本研究引入了一个新的框架,CFine,通过更好地利用CLIP的功能来改进文字图像人重新识别 (TIReID). CFine增强了细粒度的细节,以基于文本查询进行更准确的图像检索.

    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:

    • 文本图像人重新识别 (TIReID) 旨在将图像与文本描述相匹配.
    • 目前的方法在多模式通信和细节细节方面扎.
    • 像CLIP这样的视觉语言预训练模型提供了潜力,但在捕获TIReID的细微信息方面存在局限性.

    研究的目的:

    • 为提升TIReID提出一个新的CLIP驱动的细粒度信息挖掘框架 (CFine).
    • 解决利用多模式通信和细粒度信息的现有方法的局限性.
    • 提高基于TIReID中的文本查询检索图像的准确性和效率.

    主要方法:

    • 开发了一个基于CLIP的细粒度信息挖掘框架 (CFine).
    • 引入了一个多层次的全球特征学习 (MGF) 模块,以挖掘具有歧视性的本地信息并增强全球特征.
    • 设计的交叉粒度特征精制 (CFR) 和细粒度对应发现 (FCD) 模块,用于在多个层面建立交叉模式对应.

    主要成果:

    • 拟议的CFine框架有效地利用了CLIP对TIReID的知识.
    • MGF模块增强了与身份相关的歧视性线索的提取.
    • 在多个基准上的实验表明,与现有方法相比,性能优越.

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    结论:

    • CFine显著提高了文字图像人重新识别性能.
    • 该框架成功地解决了细粒度信息和跨模式通信的挑战.
    • CFine为推进TIReID研究提供了一个有前途的方向.