Research map / Label-efficient and robust learning / Domain adaptation and generalization
Generalizable person re-identification
Uses style normalization and domain-invariant mappings for person re-identification on unseen domains.
11 accepted papers at ICML, NeurIPS, ICLR, CVPR and AAAI (2019–2024) · an approach in Domain adaptation and generalization, keyword cluster “domain generalization · dg · unseen”
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Where it stands
Not enough benchmark data Fewer than four papers report the same benchmark, so there is no record to follow.
3 papers in 2023–24 and 0 in 2025–26 (at ICML, NeurIPS, ICLR, CVPR and AAAI); its share of the topic shrank, from 0.9% to 0.0%.
How this is decided
A benchmark is tracked when at least four papers of this approach report their own method on the same dataset and metric in at least two different years. Its record is the best number reported up to 2024 compared with the best reported in 2025–26: still improving means at least 1% better, flat means at most 0.2% better with at least two papers from 2025–26 trying. Numbers on a different scale from the rest (a fraction among percentages) and far outliers are left out. Results come from different papers with different setups, so they are signals, not head-to-head comparisons. The share of papers is counted at ICML, NeurIPS, ICLR, CVPR and AAAI.
How it got here
Most cited paper of each year
- 2019 · Generalizable Person Re-Identification by Domain-Invariant Mapping Network (CVPR 2019 · 237 citations)
- 2020 · Style Normalization and Restitution for Generalizable Person Re-Identification (CVPR 2020 · 439 citations)
- 2021 · Learning to Generalize Unseen Domains via Memory-based Multi-Source Meta-Learning for Person Re-Identification (CVPR 2021 · 244 citations)
- 2022 · Meta Distribution Alignment for Generalizable Person Re-Identification (CVPR 2022 · 83 citations)
- 2024 · Diversity-Authenticity Co-constrained Stylization for Federated Domain Generalization in Person Re-identification (AAAI 2024 · 9 citations)
Earliest papers here
- Generalizable Person Re-Identification by Domain-Invariant Mapping Network (CVPR 2019 · 237 citations)
- Style Normalization and Restitution for Generalizable Person Re-Identification (CVPR 2020 · 439 citations)
- Learning to Generalize Unseen Domains via Memory-based Multi-Source Meta-Learning for Person Re-Identification (CVPR 2021 · 244 citations)
What has been tried
11 papers here report 130 measured changes (a component added, removed or swapped, compared with a baseline): helped 78%, no clear effect 3%, hurt 14%, mixed 5%. Papers mostly publish changes that helped, so "helped" is over-represented.
Ablations in recent papers
| What was changed | Measured on | Result | Effect | Paper |
|---|---|---|---|---|
| Add DACS stylization | MS+C2+C3→M · mAP | 25.4 -> 36.3 | Helped | Diversity-Authenticity Co-constrained Stylization for Federated Domain Generalization in Person Re-identification (AAAI 2024 · 9 citations) |
| Apply cross-entropy loss to augmented images instead of training without augmented images | Protocol-3 average · mAP | 42.1 -> 37.7 | Hurt | Generalizable Person Re-identification via Balancing Alignment and Uniformity (NeurIPS 2024 · 9 citations) |
| add fidelity adaptive training | CUHK-SYSU · mAP | 66.7 -> 75.8 | Helped | Domain Generalizable Person Search Using Unreal Dataset (AAAI 2024 · 4 citations) |
Recent preprints
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Related approaches
Same keyword cluster
- Optimization-based domain generalization (101 papers)
- Foundation-model domain generalization (56 papers)
- Augmentation and mixture-of-experts generalization (51 papers)
Elsewhere in the topic
- Conditional distribution alignment (162 papers)
- Cross-domain few-shot with transformers (116 papers)
- Feature alignment for unsupervised adaptation (105 papers)
- Adversarial domain adaptation (95 papers)
- Geometric alignment and synthetic-to-real adaptation (93 papers)
- Source-free domain adaptation (92 papers)
- Adaptation for semantic segmentation (81 papers)
- Statistical alignment and hypothesis testing (80 papers)
Benchmark numbers, ablations and baseline numbers are extracted by AI from each paper's full text and can be wrong; the paper is the authority. Data: results index of 2026-11-02, citation counts from OpenAlex (2026-10-09).