  Person re-identification (ReID) has achieved significant improvement under
the single-domain setting. However, directly exploiting a model to new domains
is always faced with huge performance drop, and adapting the model to new
domains without target-domain identity labels is still challenging. In this
paper, we address cross-domain ReID and make contributions for both model
generalization and adaptation. First, we propose Part Aligned Pooling (PAP)
that brings significant improvement for cross-domain testing. Second, we design
a Part Segmentation (PS) constraint over ReID feature to enhance alignment and
improve model generalization. Finally, we show that applying our PS constraint
to unlabeled target domain images serves as effective domain adaptation. We
conduct extensive experiments between three large datasets, Market1501, CUHK03
and DukeMTMC-reID. Our model achieves state-of-the-art performance under both
source-domain and cross-domain settings. For completeness, we also demonstrate
the complementarity of our model to existing domain adaptation methods. The
code is available at https://github.com/huanghoujing/EANet.
