Person re-identification is a problem of identifying individuals across non-overlapping cameras. Although remarkable progress has been made in the re-identification problem, it is still a challenging problem due to appearance variations of the same person as well as other people of similar appearance. Some prior works solved the issues by separating features of positive samples from features of negative ones. However, the performances of existing models considerably depend on the characteristics and statistics of the samples used for training. Thus, we propose a novel framework named sampling independent robust feature representation network (SirNet) that learns disentangled feature embedding from randomly chosen samples. A carefully designed sampling independent maximum discrepancy loss is introduced to model samples of the same person as a cluster. As a result, the proposed framework can generate additional hard negatives/positives using the learned features, which results in better discriminability from other identities. Extensive experimental results on large-scale benchmark datasets verify that the proposed model is more effective than prior state-of-the-art models.
翻译:虽然在重新确定身份问题上取得了显著进展,但由于同一人和相近相貌的其他人的外观不同,这仍然是一个具有挑战性的问题。一些先前的工作通过将正样特征与负样特征区分开来解决问题。然而,现有模型的性能在很大程度上取决于用于培训的样本的特征和统计。因此,我们提议了一个名为取样的独立、稳健的特征代表网络(SirNet)的新框架,它从随机选择的样本中学习了分解的特征。一个精心设计的独立抽样的最大差异损失被引入一个组群的同一个人的样本模型。因此,拟议的框架能够利用所学特征产生更多的硬性负数/正数,从而与其他特征产生更好的差异。大规模基准数据集的广泛实验结果证实,拟议的模型比以前最先进的模型更有效。