Dual Cluster Contrastive learning for Person Re-Identification

Recently, cluster contrastive learning has been proven effective for person ReID by computing the contrastive loss between the individual feature and the cluster memory. However, existing methods that use the individual feature to momentum update the cluster memory are not robust to the noisy samples, such as the samples with wrong annotated labels or the pseudo-labels. Unlike the individual-based updating mechanism, the centroid-based updating mechanism that applies the mean feature of each cluster to update the cluster memory is robust against minority noisy samples. Therefore, we formulate the individual-based updating and centroid-based updating mechanisms in a unified cluster contrastive framework, named Dual Cluster Contrastive learning (DCC), which maintains two types of memory banks: individual and centroid cluster memory banks. Significantly, the individual cluster memory is momentum updated based on the individual feature. The centroid cluster memory applies the mean feature of each cluter to update the corresponding cluster memory. Besides the vallina contrastive loss for each memory, a consistency constraint is applied to guarantee the consistency of the output of two memories. Note that DCC can be easily applied for unsupervised or supervised person ReID by using ground-truth labels or pseudo-labels generated with clustering method, respectively. Extensive experiments on two benchmarks under supervised person ReID and unsupervised person ReID demonstrate the superior of the proposed DCC. Code is available at: https://github.com/htyao89/Dual-ClusterContrastive/