Apparel-invariant Feature Learning for Apparel-changed Person Re-identification

TL;DR

This paper introduces a semi-supervised framework for apparel-invariant person re-identification, utilizing a novel GAN to synthesize cloth-changing images and improve model robustness in real-world scenarios.

Contribution

It proposes the first semi-supervised apparel-invariant feature learning method with an unsupervised GAN for cloth change simulation in ReID.

Findings

Improved ReID accuracy on multiple datasets.

Effective synthesis of cloth-changing images from low-quality CCTV footage.

Enhanced robustness of ReID models to clothing variations.

Abstract

With the rise of deep learning methods, person Re-Identification (ReID) performance has been improved tremendously in many public datasets. However, most public ReID datasets are collected in a short time window in which persons' appearance rarely changes. In real-world applications such as in a shopping mall, the same person's clothing may change, and different persons may wearing similar clothes. All these cases can result in an inconsistent ReID performance, revealing a critical problem that current ReID models heavily rely on person's apparels. Therefore, it is critical to learn an apparel-invariant person representation under cases like cloth changing or several persons wearing similar clothes. In this work, we tackle this problem from the viewpoint of invariant feature representation learning. The main contributions of this work are as follows. (1) We propose the semi-supervised Apparel-invariant Feature Learning (AIFL) framework to learn an apparel-invariant pedestrian representation using images of the same person wearing different clothes. (2) To obtain images of the same person wearing different clothes, we propose an unsupervised apparel-simulation GAN (AS-GAN) to synthesize cloth changing images according to the target cloth embedding. It's worth noting that the images used in ReID tasks were cropped from real-world low-quality CCTV videos, making it more challenging to synthesize cloth changing images. We conduct extensive experiments on several datasets comparing with several baselines. Experimental results demonstrate that our proposal can improve the ReID performance of the baseline models.