  Unsupervised domain adaptation (UDA) methods for person re-identification
(re-ID) aim at transferring re-ID knowledge from labeled source data to
unlabeled target data. Although achieving great success, most of them only use
limited data from a single-source domain for model pre-training, making the
rich labeled data insufficiently exploited. To make full use of the valuable
labeled data, we introduce the multi-source concept into UDA person re-ID
field, where multiple source datasets are used during training. However,
because of domain gaps, simply combining different datasets only brings limited
improvement. In this paper, we try to address this problem from two
perspectives, \ie{} domain-specific view and domain-fusion view. Two
constructive modules are proposed, and they are compatible with each other.
First, a rectification domain-specific batch normalization (RDSBN) module is
explored to simultaneously reduce domain-specific characteristics and increase
the distinctiveness of person features. Second, a graph convolutional network
(GCN) based multi-domain information fusion (MDIF) module is developed, which
minimizes domain distances by fusing features of different domains. The
proposed method outperforms state-of-the-art UDA person re-ID methods by a
large margin, and even achieves comparable performance to the supervised
approaches without any post-processing techniques.
