# ConMAE: Contour Guided MAE for Unsupervised Vehicle Re-Identification

arXiv: https://arxiv.org/abs/2302.05673 · HTML: https://mlsanity.com/paper/2302.05673/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 38 references that are in the map.
- First posted on arXiv 11 Feb 2023.

## Builds on (most cited first, 14 of 14)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Exploring the Limits of Out-of-Distribution Detection](https://mlsanity.com/paper/2106.03004/) (2021)
- [Re-ranking Person Re-identification with k-reciprocal Encoding](https://mlsanity.com/paper/1701.08398/) (2017)
- [Self-paced Contrastive Learning with Hybrid Memory for Domain Adaptive Object Re-ID](https://mlsanity.com/paper/2006.02713/) (2020)
- [Cluster Contrast for Unsupervised Person Re-Identification](https://mlsanity.com/paper/2103.11568/) (2021)
- [Part-based Pseudo Label Refinement for Unsupervised Person Re-identification](https://mlsanity.com/paper/2203.14675/) (2022)
- [Unsupervised Vehicle Re-identification with Progressive Adaptation](https://mlsanity.com/paper/2006.11486/) (2020)
- [Exploring the Quality of GAN Generated Images for Person Re-Identification](https://mlsanity.com/paper/2108.09977/) (2021)
- [Cross Domain Knowledge Learning with Dual-branch Adversarial Network for Vehicle Re-identification](https://mlsanity.com/paper/1905.00006/) (2019)
- [Cross Domain Knowledge Transfer for Unsupervised Vehicle Re-identification](https://mlsanity.com/paper/1903.07868/) (2019)
- [Viewpoint-aware Progressive Clustering for Unsupervised Vehicle Re-identification](https://mlsanity.com/paper/2011.09099/) (2020)
- [Camera-Tracklet-Aware Contrastive Learning for Unsupervised Vehicle Re-Identification](https://mlsanity.com/paper/2109.06401/) (2021)
- [DCDLearn: Multi-order Deep Cross-distance Learning for Vehicle Re-Identification](https://mlsanity.com/paper/2003.11315/) (2020)

## Abstract (arXiv)

Vehicle re-identification is a cross-view search task by matching the same target vehicle from different perspectives. It serves an important role in road-vehicle collaboration and intelligent road control. With the large-scale and dynamic road environment, the paradigm of supervised vehicle re-identification shows limited scalability because of the heavy reliance on large-scale annotated datasets. Therefore, the unsupervised vehicle re-identification with stronger cross-scene generalization ability has attracted more attention. Considering that Masked Autoencoder (MAE) has shown excellent performance in self-supervised learning, this work designs a Contour Guided Masked Autoencoder for Unsupervised Vehicle Re-Identification (ConMAE), which is inspired by extracting the informative contour clue to highlight the key regions for cross-view correlation. ConMAE is implemented by preserving the image blocks with contour pixels and randomly masking the blocks with smooth textures. In addition, to improve the quality of pseudo labels of vehicles for unsupervised re-identification, we design a label softening strategy and adaptively update the label with the increase of training steps. We carry out experiments on VeRi-776 and VehicleID datasets, and a significant performance improvement is obtained by the comparison with the state-of-the-art unsupervised vehicle re-identification methods. The code is available on the website of https://github.com/2020132075/ConMAE.