DREAMT: Diversity Enlarged Mutual Teaching for Unsupervised Domain Adaptive Person Re-Identification

Pseudo-label-based methods of unsupervised domain adaption (UDA) can transfer the knowledge learned from a labeled source domain to an unlabeled target domain and have recently achieved significant progress in the application of person reidentification (re-ID). However, these methods suffer from serious label noise problems that downgrade the retrieval performance in UDA person re-ID. The mutual teaching framework (MTF) with dual networks attempts to tackle this problem by generating reliable soft pseudo labels but results in a mutual convergence problem. In this paper, a novel DiveRsity EnlArged Mutual Teaching framework (DREAMT) is proposed to solve the problem mentioned above. Based on the primary mutual-mean-teaching mechanism two strategies are developed in DREAMT, that is, GAN-based source domain augmentation (GSDA) and cross-branch mutual supervision (CBMS) for dual networks. Specifically, GSDA exploits two GANs to augment source domain datasets in different ways for pre-training to improve the pre-trained models’ performance and enlarge the diversity at the beginning of target domain adaption. During target adaption, each network in MTF adopts two branches to extract different features. CBMS based on hard and soft pseudo labels is across branches and networks and can help to maintain the diversity between training peers in the whole training process. Extensive experiments have demonstrated that our proposed DREAMT framework achieves better mAP and CMC performance than the existing mutual teaching methods and outperforms various state-of-the-art methods in UDA person re-ID tasks.