Pixel and Feature Transfer Fusion for Unsupervised Cross-Dataset Person Reidentification

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Volume Title

A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä

Date

2025-03-01

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Mcode

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Language

en

Pages

13

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IEEE Transactions on Neural Networks and Learning Systems, Volume 36, issue 3, pp. 4220-4232

Abstract

Recently, unsupervised cross-dataset person reidentification (Re-ID) has attracted more and more attention, which aims to transfer knowledge of a labeled source domain to an unlabeled target domain. There are two common frameworks: one is pixel-alignment of transferring low-level knowledge, and the other is feature-alignment of transferring high-level knowledge. In this article, we propose a novel recurrent autoencoder (RAE) framework to unify these two kinds of methods and inherit their merits. Specifically, the proposed RAE includes three modules, i.e., a feature-transfer (FT) module, a pixel-transfer (PT) module, and a fusion module. The FT module utilizes an encoder to map source and target images to a shared feature space. In the space, not only features are identity-discriminative but also the gap between source and target features is reduced. The PT module takes a decoder to reconstruct original images with its features. Here, we hope that the images reconstructed from target features are in the source style. Thus, the low-level knowledge can be propagated to the target domain. After transferring both high- and low-level knowledge with the two proposed modules above, we design another bilinear pooling layer to fuse both kinds of knowledge. Extensive experiments on Market-1501, DukeMTMC-ReID, and MSMT17 datasets show that our method significantly outperforms either pixel-alignment or feature-alignment Re-ID methods and achieves new state-of-the-art results.

Description

| openaire: EC/H2020/101016775/EU//INTERVENE

Keywords

Adaptation models, Cameras, Data models, Feature fusion, Image reconstruction, Lighting, Measurement, Scalability, generate adversarial nets, person reidentification (Re-ID), unsupervised learning.

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Citation

Yang, Y, Wang, G, Tiwari, P, Pandey, H M & Lei, Z 2025, 'Pixel and Feature Transfer Fusion for Unsupervised Cross-Dataset Person Reidentification', IEEE Transactions on Neural Networks and Learning Systems, vol. 36, no. 3, pp. 4220-4232. https://doi.org/10.1109/TNNLS.2021.3128269