  In this paper, we focus on model generalization and adaptation for
cross-domain person re-identification (Re-ID). Unlike existing cross-domain
Re-ID methods, leveraging the auxiliary information of those unlabeled
target-domain data, we aim at enhancing the model generalization and adaptation
by discriminative feature learning, and directly exploiting a pre-trained model
to new domains (datasets) without any utilization of the information from
target domains. To address the discriminative feature learning problem, we
surprisingly find that simply introducing the attention mechanism to adaptively
extract the person features for every domain is of great effectiveness. We
adopt two popular type of attention mechanisms, long-range dependency based
attention and direct generation based attention. Both of them can perform the
attention via spatial or channel dimensions alone, even the combination of
spatial and channel dimensions. The outline of different attentions are well
illustrated. Moreover, we also incorporate the attention results into the final
output of model through skip-connection to improve the features with both high
and middle level semantic visual information. In the manner of directly
exploiting a pre-trained model to new domains, the attention incorporation
method truly could enhance the model generalization and adaptation to perform
the cross-domain person Re-ID. We conduct extensive experiments between three
large datasets, Market-1501, DukeMTMC-reID and MSMT17. Surprisingly,
introducing only attention can achieve state-of-the-art performance, even much
better than those cross-domain Re-ID methods utilizing auxiliary information
from the target domain.
