# Online Unsupervised Domain Adaptation for Person Re-identification

arXiv: https://arxiv.org/abs/2205.04383 · HTML: https://mlsanity.com/paper/2205.04383/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 37 references that are in the map.
- First posted on arXiv 9 May 2022.

## Builds on (most cited first, 26 of 26)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Learning without Forgetting](https://mlsanity.com/paper/1606.09282/) (2016)
- [Lifelong Learning with Dynamically Expandable Networks](https://mlsanity.com/paper/1708.01547/) (2017)
- [Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking](https://mlsanity.com/paper/1609.01775/) (2016)
- [Person Transfer GAN to Bridge Domain Gap for Person Re-Identification](https://mlsanity.com/paper/1711.08565/) (2017)
- [Universal Source-Free Domain Adaptation](https://mlsanity.com/paper/2004.04393/) (2020)
- [Mutual Mean-Teaching: Pseudo Label Refinery for Unsupervised Domain Adaptation on Person Re-identification](https://mlsanity.com/paper/2001.01526/) (2020)
- [Self-paced Contrastive Learning with Hybrid Memory for Domain Adaptive Object Re-ID](https://mlsanity.com/paper/2006.02713/) (2020)
- [Compacting, Picking and Growing for Unforgetting Continual Learning](https://mlsanity.com/paper/1910.06562/) (2019)
- [Image-Image Domain Adaptation with Preserved Self-Similarity and Domain-Dissimilarity for Person Re-identification](https://mlsanity.com/paper/1711.07027/) (2017)
- [Lifelong GAN: Continual Learning for Conditional Image Generation](https://mlsanity.com/paper/1907.10107/) (2019)
- [Memory Replay GANs: learning to generate images from new categories without forgetting](https://mlsanity.com/paper/1809.02058/) (2018)
- [Unsupervised Person Re-identification: Clustering and Fine-tuning](https://mlsanity.com/paper/1705.10444/) (2017)
- [Unsupervised Domain Adaptive Re-Identification: Theory and Practice](https://mlsanity.com/paper/1807.11334/) (2018)
- [Lifelong Person Re-Identification via Adaptive Knowledge Accumulation](https://mlsanity.com/paper/2103.12462/) (2021)
- [Online Continual Learning under Extreme Memory Constraints](https://mlsanity.com/paper/2008.01510/) (2020)
- [Understanding Catastrophic Forgetting and Remembering in Continual Learning with Optimal Relevance Mapping](https://mlsanity.com/paper/2102.11343/) (2021)
- [Incremental Learning in Semantic Segmentation from Image Labels](https://mlsanity.com/paper/2112.01882/) (2021)
- [Lifelong Object Detection](https://mlsanity.com/paper/2009.01129/) (2020)
- [SF-UDA3D: Source-Free Unsupervised Domain Adaptation for LiDAR-Based 3D Object Detection](https://mlsanity.com/paper/2010.08243/) (2020)
- [Complementary Pseudo Labels For Unsupervised Domain Adaptation On Person Re-identification](https://mlsanity.com/paper/2101.12521/) (2021)
- [Lifelong Teacher-Student Network Learning](https://mlsanity.com/paper/2107.04689/) (2021)
- [Lifelong Unsupervised Domain Adaptive Person Re-identification with Coordinated Anti-forgetting and Adaptation](https://mlsanity.com/paper/2112.06632/) (2021)
- [Structured Domain Adaptation with Online Relation Regularization for Unsupervised Person Re-ID](https://mlsanity.com/paper/2003.06650/) (2020)
- [CANU-ReID: A Conditional Adversarial Network for Unsupervised person Re-IDentification](https://mlsanity.com/paper/1904.01308/) (2019)

## Built on by (most cited first, 7 of 7)

- [Identity-Seeking Self-Supervised Representation Learning for Generalizable Person Re-identification](https://mlsanity.com/paper/2308.08887/) (2023)
- [Pseudo Label Refinery for Unsupervised Domain Adaptation on Cross-dataset 3D Object Detection](https://mlsanity.com/paper/2404.19384/) (2024)
- [Source-Guided Similarity Preservation for Online Person Re-Identification](https://mlsanity.com/paper/2402.15206/) (2024)
- [Towards Effective Multi-Moving-Camera Tracking: A New Dataset and Lightweight Link Model](https://mlsanity.com/paper/2312.11035/) (2023)
- [Multi Camera Connected Vision System with Multi View Analytics: A Comprehensive Survey](https://mlsanity.com/paper/2510.09731/) (2025)
- [Real-Time Online Unsupervised Domain Adaptation for Real-World Person Re-identification](https://mlsanity.com/paper/2306.03993/) (2023)
- [Privacy-Preserving Adaptive Re-Identification without Image Transfer](https://mlsanity.com/paper/2407.12589/) (2024)

## Abstract (arXiv)

Unsupervised domain adaptation for person re-identification (Person Re-ID) is the task of transferring the learned knowledge on the labeled source domain to the unlabeled target domain. Most of the recent papers that address this problem adopt an offline training setting. More precisely, the training of the Re-ID model is done assuming that we have access to the complete training target domain data set. In this paper, we argue that the target domain generally consists of a stream of data in a practical real-world application, where data is continuously increasing from the different network's cameras. The Re-ID solutions are also constrained by confidentiality regulations stating that the collected data can be stored for only a limited period, hence the model can no longer get access to previously seen target images. Therefore, we present a new yet practical online setting for Unsupervised Domain Adaptation for person Re-ID with two main constraints: Online Adaptation and Privacy Protection. We then adapt and evaluate the state-of-the-art UDA algorithms on this new online setting using the well-known Market-1501, Duke, and MSMT17 benchmarks.