Inter-cluster and intra-cluster joint optimization for unsupervised cross-domain person re-identification

Abstract

The purpose of unsupervised cross-domain (UCD) person re-identification (re-ID) is to adapt the well pre-trained model on the labeled source domain to the unlabeled target domain, which tackles a more realistic problem. Most successful UCD methods are devoted to exploring the relationship at instance level in the target domain, but neglect the relationship at cluster level. However, person re-ID is the task of retrieving samples belonging to the same identity. Thus, exploring the cluster level relationship is critical to improve the performance of re-ID. In this paper, a novel inter-cluster and intra-cluster joint optimization (IcJO) model is proposed. Specifically, for the inter-cluster relationship, a novel centroid-based contrastive learning strategy is designed, making the sample close to its cluster centroid and far away from the heterogeneous centroids. In order to alleviate the influence of clustering noise, a cluster-based uncertainty estimation coefficient is introduced to guide the contrastive learning process. As for the intra-cluster relationship, a centroid-based maximum mean discrepancy (cMMD) loss is adopted to narrow the intra-cluster distribution. Moreover, in order to enhance the feature invariance of each identity, a centroid-based similarity loss is developed. Extensive experiments are conducted on three standard benchmarks. The results demonstrate that our IcJO model performs favorably against the state-of-the-art UCD person re-ID methods.