DCTF: Data Complementary Training Framework for Unsupervised Domain Adaptive Person Re-Identification

Unsupervised domain adaptive person reidentification (UDA re-ID) aims to transfer knowledge learned from from a source domain to a target domain. Prevalent clustering-based self-training methods suffer from label noise, which impedes improvements of model performance. To overcome this issue, mutual networks are introduced to generate reliable soft pseudo-labels for supervising the model training, allowing the suppression of label noise through the complementarity of two networks in the framework. However, existing methods based on this framework face two issues. One issue is that the complementarity between the two networks diminishes as training progresses, reducing the effectiveness of noise suppression. The other issue is that the reliability of pseudo-labels from clustering is not explicitly evaluated, and directly using all pseudo-labels for network optimization may introduce numerous noisy samples. To address these issues, we propose the Data Complementary Training Framework (DCTF) based on mutual networks. This framework comprises two main strategies. Firstly, we introduce the Camera Complementary Training Method (CCTM). CCTM utilizes camera style transfer to generate images of different camera styles for training the two networks, thereby increasing their diversity and enhancing the complementarity of mutual networks. Secondly, we propose the pseudo-labels Reliability Evaluation based on camera Style Transfer (REST). REST assesses the pseudo-label reliability based on the data distribution characteristics, selecting more reliable samples for network optimization. Experimental results on four UDA re-ID tasks demonstrate the effectiveness of our approach, with an mAP gain up to 8.7% compared to previous state-of-the-art methods on the challenging MSMT17 dataset.