The block-based representation learning method has been proven to be a very effective method for person reidentification (Re-ID), but the features extracted by the existing block-based approach tend to have a high correlation among different blocks. Also, these methods perform less well for persons with large posture changes. Thus, part-based nondirect coupling representation learning method is proposed by introducing a similarity measure loss to constrain features of different blocks. Moreover, part-based nondirect coupling embedded GAN method is proposed, which aims to extract more common features of different postures of a same person. In this way, the extracted features of the network are robust for posture changes of a person, and there are no auxiliary pose information and additional computational cost required in the test stage. Experimental results on public datasets show that our proposed method achieves good performances, especially, it outperforms the state-of-the-art GAN-based methods for person Re-ID.

中文翻译：

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PGAN：用于人员识别的基于零件的非直接耦合嵌入式GAN

基于块的表示学习方法已被证明是一种非常有效的人员重新识别（Re-ID）方法，但是现有基于块的方法提取的特征往往在不同块之间具有高度相关性。同样，这些方法对于姿势变化较大的人效果也不佳。因此，通过引入相似性度量损失来约束不同块的特征，提出了一种基于零件的非直接耦合表示学习方法。此外，提出了一种基于零件的非直接耦合嵌入式GAN方法，该方法旨在提取同一个人不同姿势的更多共同特征。以这种方式，网络的提取特征对于人的姿势改变是鲁棒的，并且在测试阶段中不需要辅助姿势信息和额外的计算成本。