HC-GCN: hierarchical contrastive graph convolutional network for unsupervised domain adaptation on person re-identification

The unsupervised domain adaptation (UDA) task on person re-identification (ReID) aims at spotting a person of interest under cross-camera by transferring the person knowledge learned from a labeled source domain to an unlabeled target domain. Recently, the contrastive loss provides an effective approach for UDA person ReID by comparing global features of the pedestrians. Generally, the fine-grained local features are favorable to distinguish the pedestrian appearance changes. However, the traditional contrastive loss-based UDA methods ignore the importance of local details and the relationship between the different granularities of features. To overcome this problem, we propose a hierarchical contrastive graph convolutional network, termed HC-GCN, for UDA person ReID. We first build an effective hierarchical graph model to learn the relationship between the global and local pedestrian features, where the local features are obtained by rough split and affine transformation. Moreover, we introduce the contrastive loss to suppress the pedestrian-irrelevant features, where the global and local contrastive losses are used. Experiments demonstrate that our method can achieve superior performance on the challenging Market-1501 and MSMT17 datasets.