  Person re-identification (re-ID) aims at recognizing the same person from
images taken across different cameras. To address this challenging task,
existing re-ID models typically rely on a large amount of labeled training
data, which is not practical for real-world applications. To alleviate this
limitation, researchers now targets at cross-dataset re-ID which focuses on
generalizing the discriminative ability to the unlabeled target domain when
given a labeled source domain dataset. To achieve this goal, our proposed Pose
Disentanglement and Adaptation Network (PDA-Net) aims at learning deep image
representation with pose and domain information properly disentangled. With the
learned cross-domain pose invariant feature space, our proposed PDA-Net is able
to perform pose disentanglement across domains without supervision in
identities, and the resulting features can be applied to cross-dataset re-ID.
Both of our qualitative and quantitative results on two benchmark datasets
confirm the effectiveness of our approach and its superiority over the
state-of-the-art cross-dataset Re-ID approaches.
