# Dual Distribution Alignment Network for Generalizable Person Re-Identification

arXiv: https://arxiv.org/abs/2007.13249 · HTML: https://mlsanity.com/paper/2007.13249/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jul 2020.

## Built on by (most cited first, 20 of 20)

- [Deep Frequency Filtering for Domain Generalization](https://mlsanity.com/paper/2203.12198/) (2022)
- [Free Lunch for Domain Adversarial Training: Environment Label Smoothing](https://mlsanity.com/paper/2302.00194/) (2023)
- [TransMatcher: Deep Image Matching Through Transformers for Generalizable Person Re-identification](https://mlsanity.com/paper/2105.14432/) (2021)
- [Learning Domain Invariant Representations for Generalizable Person Re-Identification](https://mlsanity.com/paper/2103.15890/) (2021)
- [Person Re-identification: A Retrospective on Domain Specific Open Challenges and Future Trends](https://mlsanity.com/paper/2202.13121/) (2022)
- [Identity-Seeking Self-Supervised Representation Learning for Generalizable Person Re-identification](https://mlsanity.com/paper/2308.08887/) (2023)
- [Debiased Batch Normalization via Gaussian Process for Generalizable Person Re-Identification](https://mlsanity.com/paper/2203.01723/) (2022)
- [Generalizable Person Re-Identification via Self-Supervised Batch Norm Test-Time Adaption](https://mlsanity.com/paper/2203.00672/) (2022)
- [DEX: Domain Embedding Expansion for Generalized Person Re-identification](https://mlsanity.com/paper/2110.11391/) (2021)
- [Generalizable Person Re-identification via Balancing Alignment and Uniformity](https://mlsanity.com/paper/2411.11471/) (2024)
- [CLIP-DFGS: A Hard Sample Mining Method for CLIP in Generalizable Person Re-Identification](https://mlsanity.com/paper/2410.11255/) (2024)
- [Label Distribution Learning for Generalizable Multi-source Person Re-identification](https://mlsanity.com/paper/2204.05903/) (2022)
- [Domain-Class Correlation Decomposition for Generalizable Person Re-Identification](https://mlsanity.com/paper/2106.15206/) (2021)
- [An Open-World, Diverse, Cross-Spatial-Temporal Benchmark for Dynamic Wild Person Re-Identification](https://mlsanity.com/paper/2403.15119/) (2024)
- [Feature-Distribution Perturbation and Calibration for Generalized Person ReID](https://mlsanity.com/paper/2205.11197/) (2022)
- [Generalizable Metric Network for Cross-domain Person Re-identification](https://mlsanity.com/paper/2306.11991/) (2023)
- [Calibrated Feature Decomposition for Generalizable Person Re-Identification](https://mlsanity.com/paper/2111.13945/) (2021)
- [Adaptive Domain-Specific Normalization for Generalizable Person Re-Identification](https://mlsanity.com/paper/2105.03042/) (2021)
- [Domain generalization Person Re-identification on Attention-aware multi-operation strategery](https://mlsanity.com/paper/2210.10409/) (2022)
- [Camera-Invariant Meta-Learning Network for Single-Camera-Training Person Re-identification](https://mlsanity.com/paper/2406.14797/) (2024)

## Abstract (arXiv)

Domain generalization (DG) serves as a promising solution to handle person Re-Identification (Re-ID), which trains the model using labels from the source domain alone, and then directly adopts the trained model to the target domain without model updating. However, existing DG approaches are usually disturbed by serious domain variations due to significant dataset variations. Subsequently, DG highly relies on designing domain-invariant features, which is however not well exploited, since most existing approaches directly mix multiple datasets to train DG based models without considering the local dataset similarities, i.e., examples that are very similar but from different domains. In this paper, we present a Dual Distribution Alignment Network (DDAN), which handles this challenge by mapping images into a domain-invariant feature space by selectively aligning distributions of multiple source domains. Such an alignment is conducted by dual-level constraints, i.e., the domain-wise adversarial feature learning and the identity-wise similarity enhancement. We evaluate our DDAN on a large-scale Domain Generalization Re-ID (DG Re-ID) benchmark. Quantitative results demonstrate that the proposed DDAN can well align the distributions of various source domains, and significantly outperforms all existing domain generalization approaches.