Noise Resistible Network for Unsupervised Domain Adaptation on Person Re-Identification

Abstract

Unsupervised domain adaptation on person re-identification (re-ID), which adapts the model trained on source dataset to the target dataset, has drawn increasing attention over the past few years. It is more practical than the traditional supervised methods when applied in the real-world scenarios since they require a huge number of manual annotations in a specific domain, which is unrealistic and even under personal privacy concerns. Currently, pseudo label-based method is one of the most promising solutions in this area. However, in such methods, pseudo label noise is ignored and remains a huge challenge hindering further performance improvements. To solve this problem, this paper proposes a novel unsupervised domain adaptation re-ID framework named Noise Resistible Network (NRNet), which mainly consists of two dual-stream networks. For one thing, during pseudo label generation, NRNet utilizes one dual-stream network, denoted as clustering network, to generate discriminative features in the unseen domain for further clustering, reducing the pseudo label noise. For another, to avoid the problem of close loop noise amplification in conventional methods, the other dual-stream network named temporally average network is constructed outside the clustering loop to learn how to identify the images of the same person. In addition, two dual-stream networks are designed with a guiding mechanism, which allows the shallow network to learn more representative feature embedding from the deep network. Extensive experimental results on two widely-used benchmark datasets, i.e., Market-1501 and DukeMTMC-reID demonstrate that our proposed NRNet outperforms the state-of-the-art methods.