Domain-Shared Learning and Gradual Alignment for Unsupervised Domain Adaptation Visible-Infrared Person Re-Identification
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866909915434450944 |
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| author | Huang, Nianchang Xu, Yi Xi, Ruida Xi, Ruida Zhang, Qiang |
| author_facet | Huang, Nianchang Xu, Yi Xi, Ruida Xi, Ruida Zhang, Qiang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_16184 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Domain-Shared Learning and Gradual Alignment for Unsupervised Domain Adaptation Visible-Infrared Person Re-Identification Huang, Nianchang Xu, Yi Xi, Ruida Xi, Ruida Zhang, Qiang Computer Vision and Pattern Recognition 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. |
| title | Domain-Shared Learning and Gradual Alignment for Unsupervised Domain Adaptation Visible-Infrared Person Re-Identification |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2511.16184 |