Unsupervised Person Re-Identification Via Global-Level And Patch-Level Discriminative Feature Learning

Zongzhe Sun, Feng Zhao, Feng Wu
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引用次数: 4

Abstract

Due to the lack of labeled data, it is usually difficult for an unsupervised person re-identification (re-ID) model to learn discriminative features. To address this issue, we propose a global-level and patch-level unsupervised feature learning framework that utilizes both global and local information to obtain more discriminative features. For global-level learning, we design a global similarity-based loss (GSL) to leverage the similarities between whole images. Along with a memory-based non-parametric classifier, the GSL pulls credible samples closer to help train a discriminative model. For patch-level learning, we use a patch generation module to produce different patches. Applying the patch-based discriminative feature learning loss and image-level feature learning loss, the patch branch in the network can learn better representative patch features. Combining the global-level learning with patch-level learning, we obtain a more distinguishable re-ID model. Experimental results obtained on Market-1501 and DukeMTMC-reID datasets validate that our method has great superiority and effectiveness in unsupervised person re-ID.
基于全局和局部判别特征学习的无监督人再识别
由于缺乏标记数据,无监督人再识别(re-ID)模型通常难以学习判别特征。为了解决这个问题,我们提出了一个全局级和补丁级的无监督特征学习框架,该框架利用全局和局部信息来获得更多的判别特征。对于全局学习,我们设计了一个基于全局相似性的损失(GSL)来利用整个图像之间的相似性。与基于记忆的非参数分类器一起,GSL将可信样本拉得更近,以帮助训练判别模型。对于补丁级学习,我们使用补丁生成模块来生成不同的补丁。利用基于patch的判别特征学习损失和图像级特征学习损失,网络中的patch分支可以更好地学习到具有代表性的patch特征。将全局级学习与补丁级学习相结合,得到了一个更容易区分的re-ID模型。在Market-1501和DukeMTMC-reID数据集上的实验结果验证了我们的方法在无监督人员身份识别中具有很大的优越性和有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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