Triple Adversarial Learning and Multi-View Imaginative Reasoning for Unsupervised Domain Adaptation Person Re-Identification - Li, Huafeng; Dong, Neng; Yu, Zhengtao; Tao, Dapeng; Qi, Guanqiu (IEEE transactions on circuits and systems for video technology, 2022)
Due to the importance of practical applications, unsupervised domain adaptation (UDA) person re-identification (re-ID) has attracted increasing attention. However, most of existing methods often lack the multi-view information reasoning and ignore the domain discrepancy of the pedestrian images with the same identity, which constrain the further improvement of recognition performance. So, this paper proposes a triple adversarial learning and multi-view imaginative reasoning network (TAL-MIRN) for UDA person re-ID, which consists of a multi-view imaginative reasoning module (IRM) and a triple adversarial learning module (TALM). IRM makes the classified pedestrian identity features from a single-view image extracted by a feature encoder consistent with the classification results of the aggregated multi-view pedestrian identity features, so the strong multi-view imaginative reasoning ability of the feature encoder is obtained. TALM is composed by the adversarial learning between the camera classifier and feature encoder, adversarial learning of joint distribution alignment, and adversarial learning of the difference between two classifiers used in classification. In particular, the domain-invariant features at camera level are guaranteed by the adversarial learning between the feature extractor and camera classifier. The joint alignment of identity and domain is achieved by the competition between the feature extractor and classifier integrated with identity and domain. The discriminability and robustness of the learned features are enhanced by playing a MinMax game between two different identity classifiers. Furthermore, a simple normalization operation named as cross normalization (CN) is proposed to increase both modeling and generalization capability of the proposed TAL-MIRN across multiple domains. The proposed TAL-MIRN is applied to five benchmark datasets, and the comparative experimental results confirm its superiority over the state-of-the-art methods. The related source codes is available at https://github.com/lhf12278/TALM-IRM .