Unsupervised clothing change adaptive person ReID

TL;DR

This paper introduces a novel unsupervised model called Sync-Person-Cloud ReID that effectively addresses clothing change and data label scarcity in person re-identification by leveraging sync augmentation and feature restrictions.

Contribution

It proposes a fully unsupervised clothing change person ReID pipeline with innovative sync augmentation and feature restriction techniques, advancing the state of the art.

Findings

Outperforms existing methods on clothing change ReID datasets

Demonstrates effectiveness of unsupervised approach in challenging scenarios

Validates the proposed pipeline's robustness and accuracy

Abstract

Clothing changes and lack of data labels are both crucial challenges in person ReID. For the former challenge, people may occur multiple times at different locations wearing different clothing. However, most of the current person ReID research works focus on the benchmarks in which a person's clothing is kept the same all the time. For the last challenge, some researchers try to make model learn information from a labeled dataset as a source to an unlabeled dataset. Whereas purely unsupervised training is less used. In this paper, we aim to solve both problems at the same time. We design a novel unsupervised model, Sync-Person-Cloud ReID, to solve the unsupervised clothing change person ReID problem. We developer a purely unsupervised clothing change person ReID pipeline with person sync augmentation operation and same person feature restriction. The person sync augmentation is to supply additional same person resources. These same person's resources can be used as part supervised input by same person feature restriction. The extensive experiments on clothing change ReID datasets show the out-performance of our methods.