Unsupervised Domain Adaptive Re-Identification: Theory and Practice

TL;DR

This paper develops a theoretical framework and practical self-training method for unsupervised domain adaptive re-identification, improving performance in person and vehicle re-ID tasks without labeled target data.

Contribution

It extends classification theories to re-ID, introduces assumptions on feature space, and proposes a novel self-training scheme for unsupervised domain adaptation.

Findings

Effective in unsupervised person re-ID

Effective in vehicle re-ID

Outperforms state-of-the-art methods

Abstract

We study the problem of unsupervised domain adaptive re-identification (re-ID) which is an active topic in computer vision but lacks a theoretical foundation. We first extend existing unsupervised domain adaptive classification theories to re-ID tasks. Concretely, we introduce some assumptions on the extracted feature space and then derive several loss functions guided by these assumptions. To optimize them, a novel self-training scheme for unsupervised domain adaptive re-ID tasks is proposed. It iteratively makes guesses for unlabeled target data based on an encoder and trains the encoder based on the guessed labels. Extensive experiments on unsupervised domain adaptive person re-ID and vehicle re-ID tasks with comparisons to the state-of-the-arts confirm the effectiveness of the proposed theories and self-training framework. Our code is available at \url{https://github.com/LcDog/DomainAdaptiveReID}.