Domain Camera Adaptation and Collaborative Multiple Feature Clustering for Unsupervised Person Re-ID

TL;DR

This paper introduces a novel unsupervised person re-identification method that combines domain adaptation with collaborative multi-feature clustering to improve feature representation and reduce confirmation bias.

Contribution

It proposes a new framework integrating GAN-based domain transfer, identity-preserving losses, and collaborative multi-feature clustering for better unsupervised person re-ID.

Findings

Achieves competitive results on benchmark datasets.

Effectively reduces confirmation bias in unsupervised re-ID.

Enhances feature learning through multi-granularity clustering.

Abstract

Recently unsupervised person re-identification (re-ID) has drawn much attention due to its open-world scenario settings where limited annotated data is available. Existing supervised methods often fail to generalize well on unseen domains, while the unsupervised methods, mostly lack multi-granularity information and are prone to suffer from confirmation bias. In this paper, we aim at finding better feature representations on the unseen target domain from two aspects, 1) performing unsupervised domain adaptation on the labeled source domain and 2) mining potential similarities on the unlabeled target domain. Besides, a collaborative pseudo re-labeling strategy is proposed to alleviate the influence of confirmation bias. Firstly, a generative adversarial network is utilized to transfer images from the source domain to the target domain. Moreover, person identity preserving and identity mapping losses are introduced to improve the quality of generated images. Secondly, we propose a novel collaborative multiple feature clustering framework (CMFC) to learn the internal data structure of target domain, including global feature and partial feature branches. The global feature branch (GB) employs unsupervised clustering on the global feature of person images while the Partial feature branch (PB) mines similarities within different body regions. Finally, extensive experiments on two benchmark datasets show the competitive performance of our method under unsupervised person re-ID settings.