# Adaptation and Re-Identification Network: An Unsupervised Deep Transfer Learning Approach to Person Re-Identification

arXiv: https://arxiv.org/abs/1804.09347 · HTML: https://mlsanity.com/paper/1804.09347/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Apr 2018.

## Built on by (most cited first, 9 of 9)

- [Towards Discriminative Representation Learning for Unsupervised Person Re-identification](https://mlsanity.com/paper/2108.03439/) (2021)
- [Computer Vision for Primate Behavior Analysis in the Wild](https://mlsanity.com/paper/2401.16424/) (2024)
- [Adjusting Logit in Gaussian Form for Long-Tailed Visual Recognition](https://mlsanity.com/paper/2305.10648/) (2023)
- [Improving Unsupervised Domain Adaptive Re-Identification via Source-Guided Selection of Pseudo-Labeling Hyperparameters](https://mlsanity.com/paper/2110.07897/) (2021)
- [A formal approach to good practices in Pseudo-Labeling for Unsupervised Domain Adaptive Re-Identification](https://mlsanity.com/paper/2112.12887/) (2021)
- [Rank Flow Embedding for Unsupervised and Semi-Supervised Manifold Learning](https://mlsanity.com/paper/2304.12448/) (2023)
- [Person Re-ID through Unsupervised Hypergraph Rank Selection and Fusion](https://mlsanity.com/paper/2304.14321/) (2023)
- [Learn by Guessing: Multi-Step Pseudo-Label Refinement for Person Re-Identification](https://mlsanity.com/paper/2101.01215/) (2021)
- [Domain Camera Adaptation and Collaborative Multiple Feature Clustering for Unsupervised Person Re-ID](https://mlsanity.com/paper/2208.08624/) (2022)

## Abstract (arXiv)

Person re-identification (Re-ID) aims at recognizing the same person from images taken across different cameras. To address this task, one typically requires a large amount labeled data for training an effective Re-ID model, which might not be practical for real-world applications. To alleviate this limitation, we choose to exploit a sufficient amount of pre-existing labeled data from a different (auxiliary) dataset. By jointly considering such an auxiliary dataset and the dataset of interest (but without label information), our proposed adaptation and re-identification network (ARN) performs unsupervised domain adaptation, which leverages information across datasets and derives domain-invariant features for Re-ID purposes. In our experiments, we verify that our network performs favorably against state-of-the-art unsupervised Re-ID approaches, and even outperforms a number of baseline Re-ID methods which require fully supervised data for training.