  Domain adaptive person Re-Identification (ReID) is challenging owing to the
domain gap and shortage of annotations on target scenarios. To handle those two
challenges, this paper proposes a coupling optimization method including the
Domain-Invariant Mapping (DIM) method and the Global-Local distance
Optimization (GLO), respectively. Different from previous methods that transfer
knowledge in two stages, the DIM achieves a more efficient one-stage knowledge
transfer by mapping images in labeled and unlabeled datasets to a shared
feature space. GLO is designed to train the ReID model with unsupervised
setting on the target domain. Instead of relying on existing optimization
strategies designed for supervised training, GLO involves more images in
distance optimization, and achieves better robustness to noisy label
prediction. GLO also integrates distance optimizations in both the global
dataset and local training batch, thus exhibits better training efficiency.
Extensive experiments on three large-scale datasets, i.e., Market-1501,
DukeMTMC-reID, and MSMT17, show that our coupling optimization outperforms
state-of-the-art methods by a large margin. Our method also works well in
unsupervised training, and even outperforms several recent domain adaptive
methods.
