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对基于CLIP的个人重新识别领域差距的调查

Andrea Asperti¹, Leonardo Naldi¹, Salvatore Fiorilla¹

¹Department of Informatics-Science and Engineering (DISI), University of Bologna, 40126 Bologna, Italy.

Sensors (Basel, Switzerland)

|January 25, 2025

概括

基于CLIP的模型显示,通过减少域差距,对个人重新识别 (re-id) 有希望. 增强训练数据和使用随机删除显著提高了性能.

科学领域:

计算机视觉计算机视觉
人工智能的人工智能
机器学习机器学习

背景情况:

个人重新识别 (re-id) 对于智能监控至关重要.
域间隙,或在未见数据上的性能下降,阻碍了重新识别系统.
基于CLIP的模型提供了通过多式联络预培训进行领域概括的潜力.

研究的目的:

量化分析基于CLIP的再识别系统中的域差距.
与图像编码器相比,评估CLIP在缓解域转移方面的有效性.
评估数据增强和扩展训练对再识别器强度的影响.

主要方法:

在标准基准 (Market-1501,DukeMTMC-reID,MSMT17,机场) 上基于CLIP的再贷款模型的系统性绩效评估.
测量平均平均精度 (mAP) 和排名-1精度.
对CLIP视觉文本对齐优势的分析和与图像编码器基线的比较.
评估训练集扩展和随机删除增强.

主要成果:

CLIP的视觉-文本对齐在人重新识别方面提供了优势.
通过扩展训练数据和使用随机删除,mAP平均提高了4.3%,排名-1准确度平均提高了4.0%.
该研究量化了现实世界重新识别场景中的域差挑战.

结论:

标准化的基准和系统的评估对于可重复的再利用性研究至关重要.
基于CLIP的方法显示出在各种监控应用中提高模型稳定性和通用性的潜力.
这项工作提供了关于减轻人重新识别领域差距问题的见解.

关键词:

这就是CLIP CLIP.计算机视觉计算机视觉深度学习是一种深度学习.域差距域差距域差距域差距人重新识别人重新识别

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