Unsupervised Domain Adaptive Person Re-Identification via Human Learning Imitation

TL;DR

This paper introduces a novel human learning imitation framework for unsupervised domain adaptive person re-identification, enhancing the teacher-student paradigm by mimicking human learning behaviors to improve domain adaptation.

Contribution

It proposes a new method that imitates human learning processes through adaptive material updating, selective imitation, and structural analysis, advancing unsupervised person re-ID techniques.

Findings

Effective on three benchmark datasets

Outperforms existing unsupervised methods

Demonstrates improved domain adaptation

Abstract

Unsupervised domain adaptive person re-identification has received significant attention due to its high practical value. In past years, by following the clustering and finetuning paradigm, researchers propose to utilize the teacher-student framework in their methods to decrease the domain gap between different person re-identification datasets. Inspired by recent teacher-student framework based methods, which try to mimic the human learning process either by making the student directly copy behavior from the teacher or selecting reliable learning materials, we propose to conduct further exploration to imitate the human learning process from different aspects, \textit{i.e.}, adaptively updating learning materials, selectively imitating teacher behaviors, and analyzing learning materials structures. The explored three components, collaborate together to constitute a new method for unsupervised domain adaptive person re-identification, which is called Human Learning Imitation framework. The experimental results on three benchmark datasets demonstrate the efficacy of our proposed method.