  Person re-identification (re-ID) remains challenging in a real-world
scenario, as it requires a trained network to generalise to totally unseen
target data in the presence of variations across domains. Recently, generative
adversarial models have been widely adopted to enhance the diversity of
training data. These approaches, however, often fail to generalise to other
domains, as existing generative person re-identification models have a
disconnect between the generative component and the discriminative feature
learning stage. To address the on-going challenges regarding model
generalisation, we propose an end-to-end domain adaptive attention network to
jointly translate images between domains and learn discriminative re-id
features in a single framework. To address the domain gap challenge, we
introduce an attention module for image translation from source to target
domains without affecting the identity of a person. More specifically,
attention is directed to the background instead of the entire image of the
person, ensuring identifying characteristics of the subject are preserved. The
proposed joint learning network results in a significant performance
improvement over state-of-the-art methods on several benchmark datasets.
