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基于多分支视觉变压器和自蒸的个人重新识别

Wenjie Chen¹, Kuan Yin¹, Yongsheng Wu¹

¹Artificial Intelligence and Big Data College, Chongqing College of Electronic Engineering, Chongqing, China.

Science progress

|February 5, 2024

概括

改善个人重新识别涉及优化多分支网络与在线自蒸和噪音干扰. 这提高了识别率,这对于面部识别以外的实际应用至关重要.

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) 技术已经在深度学习方面取得了进步.
目前的重新识别模型的识别率低于面部识别,限制了实际应用.
提高行人重新识别模型的准确性仍然是一个关键的研究目标.

研究的目的:

提高个人重新识别模型的识别率.
在多行业网络中探索有效的在线自蒸策略.
分析知识蒸方法和噪声干扰对模型性能的影响.

主要方法:

利用多分支网络特点,在没有额外资源的情况下高效地在线自蒸.
理论和实验分析了使用平均平方误差 (MSE) 和Kullback-Leibler (KL) 分歧的知识蒸.
引入特定噪声干扰模型重量,以提高识别精度.

主要成果:

开发了一个有效的在线自蒸计划,利用多分支机构网络信息.
提供了对MSE和KL分歧的比较分析,用于重新识别中的知识蒸.
证明噪声干扰可以显著提高模型识别率.
在四个公共人员重新识别数据集上实现了最先进的性能.

结论:

通过有效的自我蒸和噪声干扰,优化的多分支网络增强了人的重新识别.
这项研究为重新识别任务的知识蒸技术提供了宝贵的见解.
提出的方法总的来说产生了最先进的结果,推进了实际的人重新识别应用.

关键词:

深度学习是一种深度学习.人重新识别人重新识别自蒸自蒸的方法变压器的变压器是一个变压器.

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