Unsupervised dual-teacher knowledge distillation for pseudo-label refinement in domain adaptive person re-identification

Abstract

Unsupervised Domain Adaptation (UDA) in person re-identification (reID) addresses the challenge of adapting models trained on labeled source domains to unlabeled target domains, which is crucial for real-world applications. A significant problem in clustering-based UDA methods is the noise in pseudo-labels generated due to inter-domain disparities, which can degrade the performance of reID models. To address this issue, we propose the Unsupervised Dual-Teacher Knowledge Distillation (UDKD), an efficient learning scheme designed to enhance robustness against noisy pseudo-labels in UDA for person reID. The proposed UDKD method combines the outputs of two source-trained classifiers (teachers) to train a third classifier (student) using a modified soft-triplet loss-based metric learning approach. Additionally, a weighted averaging technique is employed to rectify the noise in the predicted labels generated from the teacher networks. Experimental results demonstrate that the proposed UDKD significantly improves performance in terms of mean Average Precision (mAP) and Cumulative Match Characteristic curve (Rank 1, 5, and 10). Specifically, UDKD achieves an mAP of 84.57 and 73.32, and Rank 1 scores of 94.34 and 88.26 for Duke to Market and Market to Duke scenarios, respectively. These results surpass the state-of-the-art performance, underscoring the efficacy of UDKD in advancing UDA techniques for person reID and highlighting its potential to enhance performance and robustness in real-world applications.