Bai Xuemei, Zhang Yuqing, Zhang Chenjie, Wang Zhijun
School of Electronic Information Engineering, Changchun University of Science and Technology, Changchun, Jinlin, China.
High Performance Computing Center, Changchun Normal University, Changchun, Jinlin, China.
PLoS One. 2025 Jul 14;20(7):e0328131. doi: 10.1371/journal.pone.0328131. eCollection 2025.
Person re-identification (ReID) technology has many applications in intelligent surveillance and public safety. However, the domain difference between the source and target domains makes the generalization ability of the model extremely challenging. To reduce the dependence on labeled data, Unsupervised Domain Adaptation (UDA) methods have become an effective way to solve this problem. However, the influence of pseudo-label generated noise on model training in existing UDA methods is still significant, resulting in limited model performance on the target domain. For this reason, this paper proposes a contrast learning-based pseudo-label refinement with probabilistic uncertainty in the unsupervised domain, adapted to Person re-identification, aiming to improve the effectiveness of the unsupervised domain adapted to Person re-identification. We first enhance the feature representation of the target domain samples based on the contrast learning technique to improve their discrimination in the feature space, thereby enhancing the cross-domain migration performance of the model. Subsequently, an innovative loss function is proposed to effectively reduce the interference of label noise on the training process by refining the generation process of pseudo-labels, which solves the negative impact of inaccurate pseudo-labels on model training. Through a series of experimental validation, the method experiments on two large-scale public datasets, Market1501 and DukeMTMC, and the Rank-1 accuracy of the proposed method reaches 91.4% and 81.4%, with the mean average precision (mAP) of 79.0% and 67.9%, respectively, which proves that the research in this paper provides a good solution for the Person re-identification task with effective technical support for label noise processing and model generalization capability improvement.
行人重识别（ReID）技术在智能监控和公共安全领域有诸多应用。然而，源域和目标域之间的领域差异使得模型的泛化能力极具挑战性。为了减少对标记数据的依赖，无监督域适应（UDA）方法已成为解决此问题的有效途径。然而，现有UDA方法中伪标签生成噪声对模型训练的影响仍然很大，导致模型在目标域上的性能有限。因此，本文提出了一种基于对比学习的无监督域中具有概率不确定性的伪标签细化方法，适用于行人重识别，旨在提高无监督域适应行人重识别的有效性。我们首先基于对比学习技术增强目标域样本的特征表示，以提高其在特征空间中的辨别能力，从而增强模型的跨域迁移性能。随后，提出了一种创新的损失函数，通过细化伪标签的生成过程来有效减少标签噪声对训练过程的干扰，解决了不准确伪标签对模型训练的负面影响。通过一系列实验验证，该方法在两个大规模公共数据集Market1501和DukeMTMC上进行实验，所提方法的Rank-1准确率分别达到91.4%和81.4%，平均精度均值（mAP）分别为79.0%和67.9%，这证明本文的研究为行人重识别任务提供了一个很好的解决方案，为标签噪声处理和模型泛化能力提升提供了有效的技术支持。
