  Unsupervised domain adaptive person Re-IDentification (ReID) is challenging
because of the large domain gap between source and target domains, as well as
the lackage of labeled data on the target domain. This paper tackles this
challenge through jointly enforcing visual and temporal consistency in the
combination of a local one-hot classification and a global multi-class
classification. The local one-hot classification assigns images in a training
batch with different person IDs, then adopts a Self-Adaptive Classification
(SAC) model to classify them. The global multi-class classification is achieved
by predicting labels on the entire unlabeled training set with the Memory-based
Temporal-guided Cluster (MTC). MTC predicts multi-class labels by considering
both visual similarity and temporal consistency to ensure the quality of label
prediction. The two classification models are combined in a unified framework,
which effectively leverages the unlabeled data for discriminative feature
learning. Experimental results on three large-scale ReID datasets demonstrate
the superiority of proposed method in both unsupervised and unsupervised domain
adaptive ReID tasks. For example, under unsupervised setting, our method
outperforms recent unsupervised domain adaptive methods, which leverage more
labels for training.
