  Pedestrian attributes, e.g., hair length, clothes type and color, locally
describe the semantic appearance of a person. Training person re-identification
(ReID) algorithms under the supervision of such attributes have proven to be
effective in extracting local features which are important for ReID. Unlike
person identity, attributes are consistent across different domains (or
datasets). However, most of ReID datasets lack attribute annotations. On the
other hand, there are several datasets labeled with sufficient attributes for
the case of pedestrian attribute recognition. Exploiting such data for ReID
purpose can be a way to alleviate the shortage of attribute annotations in ReID
case. In this work, an unsupervised domain adaptive ReID feature learning
framework is proposed to make full use of attribute annotations. We propose to
transfer attribute-related features from their original domain to the ReID one:
to this end, we introduce an adversarial discriminative domain adaptation
method in order to learn domain invariant features for encoding semantic
attributes. Experiments on three large-scale datasets validate the
effectiveness of the proposed ReID framework.
