Unsupervised Disentanglement GAN for Domain Adaptive Person Re-Identification

Abstract

While recent person re-identification (ReID) methods achieve high accuracy in a supervised setting, their generalization to an unlabelled domain is still an open problem. In this paper, we introduce a novel unsupervised disentanglement generative adversarial network (UD-GAN) to address the domain adaptation issue of supervised person ReID. Our framework jointly trains a ReID network for discriminative features extraction in a source labelled domain using identity annotation, and adapts the ReID model to an unlabelled target domain by learning disentangled latent representations on the domain. Identity-unrelated features in the target domain are distilled from the latent features. As a result, the ReID features better encompass the identity of a person in the unsupervised domain. We conducted experiments on the Market1501, DukeMTMC and MSMT17 datasets. Results show that the unsupervised domain adaptation problem in ReID is very challenging. Nevertheless, our method shows improvement in half of the domain transfers and achieve state-of-the-art performance for one of them.

arXiv comment8 pages, 5 figures, submitted to ICPR 2020

References in corpus (12)

• Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
• Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
• Instance Normalization: The Missing Ingredient for Fast Stylization
• In Defense of the Triplet Loss for Person Re-Identification
• On the Convergence of Adam and Beyond
• Person Re-identification: Past, Present and Future
• AlignedReID: Surpassing Human-Level Performance in Person Re-Identification
• FD-GAN: Pose-guided Feature Distilling GAN for Robust Person Re-identification
• Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking
• Learning Generalisable Omni-Scale Representations for Person Re-Identification
• Learning Disentangled Representation for Robust Person Re-identification
• In Defense of the Classification Loss for Person Re-Identification