Cross Dataset Person Re Identification via Unsupervised Pose Disentanglement and Adaptation
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.
2019 IEEE/CVF International Conference on Computer Vision (ICCV)