Awesome-Domain-Generalizable-Person-Re-ID

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# Awecome-Domain-Generalizable-Person-Re-identification
**"Domain Generalization for Person Re-identification: A Survey Towards Domain-Agnostic Person Matching"** (Neurocomputing 2025).

[![awesome](https://img.shields.io/badge/awesome-yes-critical?style=flat&logo=awesome-lists&labelColor=purple)](https://github.com/sindresorhus/awesome)
[![Visitors](https://visitor-badge.laobi.icu/badge?page_id=PerceptualAI-Lab/Awesome-Domain-Generalizable-Person-Re-ID)](https://github.com/PerceptualAI-Lab/Awesome-Domain-Generalizable-Person-Re-ID)
[![arXiv](https://img.shields.io/badge/arXiv-Preprint-b31b1b.svg)](https://arxiv.org/abs/2506.12413)
[![Stars](https://img.shields.io/github/stars/PerceptualAI-Lab/Awesome-Domain-Generalizable-Person-Re-ID.svg?style=social&label=Star)](https://raw.githubusercontent.com/PerceptualAI-Lab/Awesome-Domain-Generalizable-Person-Re-ID/main/[https:/github.com/PerceptualAI-Lab/Awesome-Domain-Generalizable-Person-Re-ID])

---

## 📚 Contents

- [📄 Paper List](#-paper-list)
- [📊 Datasets & Benchmarks](#-datasets--benchmarks)
- [📣 News](#-news)
- [🔍 Survey Paper](#-survey-paper)
- [🔖 Citation](#-citation)

## 📄 Paper List

We categorize DG-ReID papers based on their methodologies:

## Normalization-based
<table>
<thead>
<tr>
<th align="left">Title</th>
<th align="center">Publication</th>
<th align="center">Date</th>
</tr>
</thead>
<tbody>
  <tr><td align="left"><a href="https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=44281](https://openaccess.thecvf.com/content_CVPR_2020/papers/Jin_Style_Normalization_and_Restitution_for_Generalizable_Person_Re-Identification_CVPR_2020_paper.pdf">Style Normalization and Restitution for Generalizable Person Re-Identification</a></td><td align="center">CVPR</td><td align="center">2020</td></tr>
  <tr><td align="left"><a href="https://openaccess.thecvf.com/content/CVPR2021/papers/Choi_Meta_Batch-Instance_Normalization_for_Generalizable_Person_Re-Identification_CVPR_2021_paper.pdf">Meta Batch-Instance Normalization for Generalizable Person Re-Identification</a></td><td align="center">CVPR</td><td align="center">2021</td></tr>
  <tr><td align="left"><a href="https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136740209.pdf">Adaptive Cross-Domain Learning for Generalizable Person Re-Identification</a></td><td align="center">ECCV</td><td align="center">2022</td></tr>
  <tr><td align="left"><a href="https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136740279.pdf">Dynamically Transformed Instance Normalization Network for Generalizable Person Re-Identification</a></td><td align="center">ECCV</td><td align="center">2022</td></tr>
  <tr><td align="left"><a href="https://www.ecva.net/papers/eccv_2024/papers_ECCV/papers/08753.pdf">Rethinking Normalization Layers for Domain Generalizable Person Re-identification</a></td><td align="center">ECCV</td><td align="center">2022</td></tr>
</tbody>
</table>

## Mixture-of-Experts-based
<table>
<thead>
<tr>
<th align="left">Title</th>
<th align="center">Publication</th>
<th align="center">Date</th>
</tr>
</thead>
<tbody>
  <tr><td align="left"><a href="https://openaccess.thecvf.com/content/CVPR2021/papers/Dai_Generalizable_Person_Re-Identification_With_Relevance-Aware_Mixture_of_Experts_CVPR_2021_paper.pdf">Generalizable Person Re-identification with Relevance-aware Mixture of Experts</a></td><td align="center">CVPR</td><td align="center">2021</td></tr>
  <tr><td align="left"><a href="https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136740362.pdf">Mimic Embedding via Adaptive Aggregation: Learning Generalizable Person Re-identification</a></td><td align="center">ECCV</td><td align="center">2022</td></tr>
</tbody>
</table>

## Memory-based
<table>
<thead>
<tr>
<th align="left">Title</th>
<th align="center">Publication</th>
<th align="center">Date</th>
</tr>
</thead>
<tbody>
  <tr><td align="left"><a href="https://openaccess.thecvf.com/content_CVPR_2019/papers/Song_Generalizable_Person_Re-Identification_by_Domain-Invariant_Mapping_Network_CVPR_2019_paper.pdf">Generalizable Person Re-identification by Domain-Invariant Mapping Network</a></td><td align="center">CVPR</td><td align="center">2019</td></tr>
  <tr><td align="left"><a href="https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123560443.pdf">Interpretable and Generalizable Person Re-identification with Query-adaptive Convolution and Temporal Lifting</a></td><td align="center">ECCV</td><td align="center">2020</td></tr>
  <tr><td align="left"><a href="https://openaccess.thecvf.com/content/ICCV2023/papers/Ni_Part-Aware_Transformer_for_Generalizable_Person_Re-identification_ICCV_2023_paper.pdf">Part-Aware Transformer for Generalizable Person Re-identification</a></td><td align="center">ICCV</td><td align="center">2023</td></tr>
</tbody>
</table>

## Meta-learning-based
<table>
<thead>
<tr>
<th align="left">Title</th>
<th align="center">Publication</th>
<th align="center">Date</th>
</tr>
</thead>
<tbody>
  <tr><td align="left"><a href="https://openaccess.thecvf.com/content/CVPR2021/papers/Bai_Person30K_A_Dual-Meta_Generalization_Network_for_Person_Re-Identification_CVPR_2021_paper.pdf">Person30K: A Dual-Meta Generalization Network for Person Re-Identification</a></td><td align="center">CVPR</td><td align="center">2021</td></tr>
  <tr><td align="left"><a href="https://openaccess.thecvf.com/content/CVPR2021/papers/Zhao_Learning_to_Generalize_Unseen_Domains_via_Memory-based_Multi-Source_Meta-Learning_for_CVPR_2021_paper.pdf">Learning to Generalize Unseen Domains via Memory-based Multi-Source Meta-Learning for Person Re-Identification</a></td><td align="center">CVPR</td><td align="center">2021</td></tr>
  <tr><td align="left"><a href="https://openaccess.thecvf.com/content/CVPR2022/papers/Ni_Meta_Distribution_Alignment_for_Generalizable_Person_Re-Identification_CVPR_2022_paper.pdf">Meta distribution alignment for generalizable person re-identification</a></td><td align="center">CVPR</td><td align="center">2022</td></tr>
</tbody>
</table>

## Data-driven learning-based 
<table>
<thead>
<tr>
<th align="left">Title</th>
<th align="center">Publication</th>
<th align="center">Date</th>
</tr>
</thead>
<tbody>
  <tr><td align="left"><a href="https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_Cloning_Outfits_From_Real-World_Images_to_3D_Characters_for_Generalizable_CVPR_2022_paper.pdf">Cloning Outfits From Real-World Images to 3D Characters for Generalizable Person Re-Identification</a></td><td align="center">CVPR</td><td align="center">2022</td></tr>
  <tr><td align="left"><a href="https://openaccess.thecvf.com/content/ICCV2023/papers/Dou_Identity-Seeking_Self-Supervised_Representation_Learning_for_Generalizable_Person_Re-Identification_ICCV_2023_paper.pdf">Identity-Seeking Self-Supervised Representation Learning for Generalizable Person Re-identification</a></td><td align="center">ICCV</td><td align="center">2023</td></tr>
  <tr><td align="left"><a href="https://arxiv.org/pdf/2411.11471">Generalizable Person Re-identification via Balancing Alignment and Uniformity</a></td><td align="center">NeurIPS</td><td align="center">2024</td></tr>
</tbody>
</table>

## CLIP-based
<table>
<thead>
<tr>
<th align="left">Title</th>
<th align="center">Publication</th>
<th align="center">Date</th>
</tr>
</thead>
<tbody>
  <tr><td align="left"><a href="https://dl.acm.org/doi/full/10.1145/3701036">CLIP-DFGS: A Hard Sample Mining Method for CLIP in Generalizable Person Re-Identification</a></td><td align="center">ACM TOMM</td><td align="center">2024</td></tr>
  <tr><td align="left"><a href="https://ieeexplore.ieee.org/abstract/document/10858181">CILP-FGDI: Exploiting Vision-Language Model for Generalizable Person Re-Identification</a></td><td align="center">IEEE Transactions on Information Forensics and Security</td><td align="center">2025</td></tr>
</tbody>
</table>

## Others
<table>
<thead>
<tr>
<th align="left">Title</th>
<th align="center">Publication</th>
<th align="center">Date</th>
</tr>
</thead>
<tbody>
  <tr><td align="left"><a href="https://openaccess.thecvf.com/content/WACV2024/papers/Li_Mitigate_Domain_Shift_by_Primary-Auxiliary_Objectives_Association_for_Generalizing_Person_WACV_2024_paper.pdf">Mitigate Domain Shift by Primary-Auxiliary Objectives Association for Generalizing Person ReID</a></td><td align="center">WACV</td><td align="center">2024</td></tr>
  <tr><td align="left"><a href="https://ojs.aaai.org/index.php/AAAI/article/view/28468">Diversity-Authenticity Co-constrained Stylization for Federated Domain Generalization in Person Re-identification</a></td><td align="center">AAAI</td><td align="center">2024</td></tr>
</tbody>
</table>

## 📊 Datasets & Benchmarks

We include commonly used datasets for evaluating DG-ReID performance.

| Dataset    | Venue   | Type | #ID                | #Image        | #Cam | URL                                           | Paper |
| ---------- | ------- | ------- | ------------------------- | ------------ | -------------- | --------------------------------------- | ---------------------- |
| CUHK01  | ACCV'12   | 🔹 Train | 971 | 3,884 | 2 | [🔗]() | [🔗](https://link.springer.com/chapter/10.1007/978-3-642-37331-2_3) Human re-identification with transferred metric learning
| CUHK02     | ACCV'12 | 🔹 Train  | 1,816 | 7,264 | 10 | [🔗]() | [🔗](https://link.springer.com/chapter/10.1007/978-3-642-37331-2_3) Human re-identification with transferred metric learning
| CUHK03   | CVPR'14 | 🔹 Train  | 1,476 | 13,164 | 2 | [🔗]()             | [🔗](https://openaccess.thecvf.com/content_cvpr_2014/papers/Li_DeepReID_Deep_Filter_2014_CVPR_paper.pdf) Deep filter pairing neural network for person re-identification
| Market-1501   | ICCV'15 | 🔹 Train  | 1,501 | 32,688 | 6 | [🔗]()         | [🔗](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Zheng_Scalable_Person_Re-Identification_ICCV_2015_paper.pdf) Scalable person re-identification: A benchmark
| CUHK-SYSU      | CVPR'17 | 🔹 Train  | 8,432 | 18,184 | 1 | [🔗]() | [🔗](https://openaccess.thecvf.com/content_cvpr_2017/papers/Xiao_Joint_Detection_and_CVPR_2017_paper.pdf) Joint detection and identification feature learning for person search
| Duke-MTMC      | ICCV'17 | 🔹 Train  | 1,404 | 36,411 | 8 | [🔗]() | [🔗](https://openaccess.thecvf.com/content_ICCV_2017/papers/Zheng_Unlabeled_Samples_Generated_ICCV_2017_paper.pdf) Unlabeled samples generated by gan improve the person re-identification baseline in vitro
| MSMT17      | CVPR'18 | 🔹 Train  | 4,101 | 126,441 | 15 | [🔗]() | [🔗](https://openaccess.thecvf.com/content_cvpr_2018/papers/Wei_Person_Transfer_GAN_CVPR_2018_paper.pdf) Person transfer gan to bridge domain gap for person re-identification
| PRID2011 | SCIA'11 | 🔸 Test  | 200 | 1,134 | 2 | [🔗]()         | [🔗](https://link.springer.com/chapter/10.1007/978-3-642-21227-7_9) Person re-identification by descriptive and discriminative classification
| VIPeR | PETS'07 | 🔸 Test  | 632 | 1,264 | 2 | [🔗]() | [🔗](https://www.researchgate.net/profile/Douglas-Gray-2/publication/228345677_Evaluating_appearance_models_for_recognition_reacquisition_and_tracking/links/0a85e5348dbc2ed2ec000000/Evaluating-appearance-models-for-recognition-reacquisition-and-tracking.pdf) Evaluating appearance models for recognition, reacquisition, and tracking
| iLIDS | ECCV'18 | 🔸 Test  | 119 | 476 | 2 | [🔗]() | [🔗](https://openaccess.thecvf.com/content_ECCV_2018/papers/Minxian_Li_Unsupervised_Person_Re-identification_ECCV_2018_paper.pdf) Unsupervised person re-identification by deeplearning tracklet association
| GRID | ICIP'13 | 🔸 Test  | 250 | 1,275 | 8 | [🔗]() | [🔗](https://ieeexplore.ieee.org/abstract/document/6738736) Person re-identification by manifold ranking

---

## 📣 News

- 📌 2025-06: Paper accepted on Neurocomputing and repository initialized
- 🚀 2025-05: Repository initialized.

---

## 🔍 Survey Paper

You can find the preprint of our survey [here](https://arxiv.org/abs/2506.12413)

The overview of our survey paper:
![taxonomy](https://github.com/PerceptualAI-Lab/Awesome-Domain-Generalizable-Person-Re-ID/blob/main/figures/taxonomy.png)

---

## 🔖 Citation

If you find this survey helpful, please consider citing us:

```citation
@article{lee2025domain,
  title={Domain generalization for person re-identification: A survey towards domain-agnostic person matching},
  author={Lee, Hyeonseo and Park, Juhyun and Oh, Jihyong and Eom, Chanho},
  journal={Neurocomputing},
  pages={130763},
  year={2025},
  publisher={Elsevier}
}
```

---
## 💫 Star History

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