Deep Mutual Distillation for Unsupervised Domain Adaptation Person Re-Identification

Abstract

Unsupervised domain adaptation person re-identification (UDA person re-ID) aims at transferring the knowledge on the source domain with expensive manual annotation to the unlabeled target domain. Most of the recent papers leverage pseudo-labels for the target images to accomplish this task. However, the noise in the generated labels hinders the identification system from learning discriminative features. To address this problem, we propose a deep mutual distillation (DMD) to generate reliable pseudo-labels for UDA person re-ID. The proposed DMD applies two parallel branches for feature extraction, and each branch serves as the teacher of the other to generate pseudo-labels for its training. This mutually reinforcing optimization framework enhances the reliability of pseudo-labels, improving the identification performance. In addition, we present a bilateral graph representation (BGR) to describe the pedestrian images. BGR mimics the person re-identification of the human to aggregate the identity features according to the visual similarity and attribute consistency. Experimental results on Market-1501 and Duke demonstrate the effectiveness and generalization of the proposed method.