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相关概念视频

Deindividuation00:57

Deindividuation

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Deindividuation is a form of social influence on an individual’s behavior such that the individual engages in unusual or non-normal behavior while in a group setting. Why? Because in these group settings, the individual no longer sees themselves as an individual anymore, disinhibiting their behavior and personal restraint.
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为无监督人员重新识别进行脱而出的样本指导学习.

Haoxuanye Ji, Le Wang, Sanping Zhou

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
    |September 12, 2024
    PubMed
    概括

    本研究介绍了无监督人员重新识别 (Re-ID) 的解样本指导学习 (DSGL). DSGL有效地选择了硬,高可信度样本,大大提高了对基准数据集的Re-ID准确性.

    科学领域:

    • 计算机视觉 计算机视觉
    • 机器学习 机器学习
    • 人工智能的人工智能

    背景情况:

    • 无监督人员重新识别 (Re-ID) 面临挑战,原因是缺乏基准真相标签.
    • 现有的方法往往依赖于代聚类来生成伪标签,为有效的学习而努力选择样本.

    研究的目的:

    • 提出一种新的解样本指导学习 (DSGL) 方法,以增强无监督的Re-ID.
    • 为解决选择高信心和歧视性样本进行培训的关键问题.

    主要方法:

    • DSGL包括解样本挖掘 (DSM) 来分离图像中的身份相关和无关因素.
    • DSM为歧视性信息提取构建了脱而出的正/负组.
    • 歧视性特征学习 (DFL) 整合了这些群体,使用专门的损失函数和规范化来改善个人的独特性.

    主要成果:

    • 在市场-1501上,DSGL显著提高了mAP的6.6% (ResNet50) 和0.6% (ViT).
    • 在MSMT17.上观察到10.1% (ResNet50) 和6.9% (ViT) 的mAP改善.
    • 在Market-1501,MSMT17,PersonX和VeRi-776数据集上,DSGL的性能优于最先进的方法.

    结论:

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    • 拟议的DSGL方法有效地提高了无监督人员的Re-ID性能.
    • DSGL对样本选择和特征学习的方法提高了模型区分个体的能力.
    • 该方法在多个基准数据集中显示出卓越的结果,验证了其有效性.