Domain-Shared Learning and Gradual Alignment for Unsupervised Domain Adaptation Visible-Infrared Person Re-Identification

TL;DR

This paper introduces DSLGA, a two-stage unsupervised domain adaptation method for visible-infrared person re-identification, effectively addressing modality discrepancies and improving real-world application performance.

Contribution

The paper proposes a novel two-stage framework, DSLGA, combining domain-shared learning and gradual alignment to enhance unsupervised domain adaptation in VI-ReID.

Findings

Significantly outperforms existing domain adaptation methods.

Achieves comparable or better results than some supervised methods.

Effectively handles inter- and intra-domain modality discrepancies.

Abstract

Recently, Visible-Infrared person Re-Identification (VI-ReID) has achieved remarkable performance on public datasets. However, due to the discrepancies between public datasets and real-world data, most existing VI-ReID algorithms struggle in real-life applications. To address this, we take the initiative to investigate Unsupervised Domain Adaptation Visible-Infrared person Re-Identification (UDA-VI-ReID), aiming to transfer the knowledge learned from the public data to real-world data without compromising accuracy and requiring the annotation of new samples. Specifically, we first analyze two basic challenges in UDA-VI-ReID, i.e., inter-domain modality discrepancies and intra-domain modality discrepancies. Then, we design a novel two-stage model, i.e., Domain-Shared Learning and Gradual Alignment (DSLGA), to handle these discrepancies. In the first pre-training stage, DSLGA introduces a Domain-Shared Learning Strategy (DSLS) to mitigate ineffective pre-training caused by inter-domain modality discrepancies via exploiting shared information between the source and target domains. While, in the second fine-tuning stage, DSLGA designs a Gradual Alignment Strategy (GAS) to handle the cross-modality alignment challenges between visible and infrared data caused by the large intra-domain modality discrepancies through a cluster-to-holistic alignment way. Finally, a new UDA-VI-ReID testing method i.e., CMDA-XD, is constructed for training and testing different UDA-VI-ReID models. A large amount of experiments demonstrate that our method significantly outperforms existing domain adaptation methods for VI-ReID and even some supervised methods under various settings.