# Unsupervised Vehicle Re-identification with Progressive Adaptation

arXiv: https://arxiv.org/abs/2006.11486 · HTML: https://mlsanity.com/paper/2006.11486/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jun 2020.

## Built on by (most cited first, 11 of 11)

- [Unsupervised Vehicle Re-Identification via Self-supervised Metric Learning using Feature Dictionary](https://mlsanity.com/paper/2103.02250/) (2021)
- [Camera-Tracklet-Aware Contrastive Learning for Unsupervised Vehicle Re-Identification](https://mlsanity.com/paper/2109.06401/) (2021)
- [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)
- [Scene-Adaptive Person Search via Bilateral Modulations](https://mlsanity.com/paper/2405.02834/) (2024)
- [Fast One-Stage Unsupervised Domain Adaptive Person Search](https://mlsanity.com/paper/2405.02832/) (2024)
- [Unsupervised Domain Adaptive Person Search via Dual Self-Calibration](https://mlsanity.com/paper/2412.16506/) (2024)
- [Boosting Adversarial Transferability via Residual Perturbation Attack](https://mlsanity.com/paper/2508.05689/) (2025)
- [ConMAE: Contour Guided MAE for Unsupervised Vehicle Re-Identification](https://mlsanity.com/paper/2302.05673/) (2023)
- [Revisiting Multi-Granularity Representation via Group Contrastive Learning for Unsupervised Vehicle Re-identification](https://mlsanity.com/paper/2410.21667/) (2024)
- [CORE-ReID V2: Advancing the Domain Adaptation for Object Re-Identification with Optimized Training and Ensemble Fusion](https://mlsanity.com/paper/2508.04036/) (2025)

## Abstract (arXiv)

Vehicle re-identification (reID) aims at identifying vehicles across different non-overlapping cameras views. The existing methods heavily relied on well-labeled datasets for ideal performance, which inevitably causes fateful drop due to the severe domain bias between the training domain and the real-world scenes; worse still, these approaches required full annotations, which is labor-consuming. To tackle these challenges, we propose a novel progressive adaptation learning method for vehicle reID, named PAL, which infers from the abundant data without annotations. For PAL, a data adaptation module is employed for source domain, which generates the images with similar data distribution to unlabeled target domain as ``pseudo target samples''. These pseudo samples are combined with the unlabeled samples that are selected by a dynamic sampling strategy to make training faster. We further proposed a weighted label smoothing (WLS) loss, which considers the similarity between samples with different clusters to balance the confidence of pseudo labels. Comprehensive experimental results validate the advantages of PAL on both VehicleID and VeRi-776 dataset.