Unsupervised Person Re-Identification for Multigrained Teacher-Student Networks Incorporating Spatial Frequency Information

Unsupervised person Re-Identification (Re-ID) aims to mine discriminative representations from unlabeled data for person retrieval. Currently, unsupervised person Re-ID methods based on pseudo-labels have achieved remarkable progress. However, the noise introduced during the training process and incomplete utilization of information limit its further development. This paper proposes a multigrained teacher-student network that integrates shallow spatial and frequency information. First, it simultaneously considers global and local features and integrates them into clustering-based contrastive learning, enriching feature representation. A well-trained teacher model is used to guide the student model to converge quickly, thereby reducing the interference of noisy pseudo-labels. Second, a novel spatial frequency interaction module that utilizes useful information in the shallow spatial and frequency domains that is lost during the network deepening process is proposed. Additionally, a recycling strategy is adopted in the training process of the student network, in which some unclustered instances that are directly discarded in the previous methods are recycled as hard samples. The mean Average Precision (mAP) results for three large datasets, Market1501, DukeMTMC-reID, and MSMT17, reach 87.5%, 74.8%, and 41.9%, respectively, proving the superiority of the proposed method.