Unsupervised Domain Adaptive Person Re-id with Local-enhance and Prototype Dictionary Learning

The unsupervised domain adaptive person re-identification (re-ID) task has been a challenge because, unlike the general domain adaptive tasks, there is no overlap between the classes of source and target domain data in the person re-ID, which leads to a significant domain gap. State-of-the-art unsupervised re-ID methods train the neural networks using a memory-based contrastive loss. However, performing contrastive learning by treating each unlabeled instance as a class will lead to the problem of class collision, and the updating intensity is inconsistent due to the difference in the number of instances of different categories when updating in the memory bank. To address such problems, we propose Prototype Dictionary Learning for person re-ID which is able to utilize both source domain data and target domain data by one training stage while avoiding the problem of class collision and the problem of updating intensity inconsistency by cluster-level prototype dictionary learning. In order to reduce the interference of domain gap on the model, we propose a local-enhance module to improve the domain adaptation of the model without increasing the number of model parameters. Our experiments on two large datasets demonstrate the effectiveness of the prototype dictionary learning. 71.5% mAP is achieved in the Market-to-Duke task, which is a 2.3% improvement compared to the state-of-the-art unsupervised domain adaptive re-ID methods. It achieves 83.9% mAP in the Duke-to-Market task, which improves by 4.4% compared to the state-of-the-art unsupervised adaptive re-ID methods.