# Unsupervised Domain Adaptation with Dynamic Clustering and Contrastive Refinement for Gait Recognition

arXiv: https://arxiv.org/abs/2501.16608 · HTML: https://mlsanity.com/paper/2501.16608/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 28 of its 50 references that are in the map.
- First posted on arXiv 28 Jan 2025.

## Builds on (most cited first, 28 of 28)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [Unsupervised Learning of Visual Features by Contrasting Cluster Assignments](https://mlsanity.com/paper/2006.09882/) (2020)
- [Unsupervised Domain Adaptation by Backpropagation](https://mlsanity.com/paper/1409.7495/) (2014)
- [Deep Mutual Learning](https://mlsanity.com/paper/1706.00384/) (2017)
- [Harmonious Attention Network for Person Re-Identification](https://mlsanity.com/paper/1802.08122/) (2018)
- [Universal Domain Adaptation through Self Supervision](https://mlsanity.com/paper/2002.07953/) (2020)
- [Gait Recognition in the Wild with Dense 3D Representations and A Benchmark](https://mlsanity.com/paper/2204.02569/) (2022)
- [Cluster Contrast for Unsupervised Person Re-Identification](https://mlsanity.com/paper/2103.11568/) (2021)
- [OpenGait: Revisiting Gait Recognition Toward Better Practicality](https://mlsanity.com/paper/2211.06597/) (2022)
- [Gait Recognition via Disentangled Representation Learning](https://mlsanity.com/paper/1904.04925/) (2019)
- [GaitSet: Cross-view Gait Recognition through Utilizing Gait as a Deep Set](https://mlsanity.com/paper/2102.03247/) (2021)
- [DyGait: Exploiting Dynamic Representations for High-performance Gait Recognition](https://mlsanity.com/paper/2303.14953/) (2023)
- [Parsing is All You Need for Accurate Gait Recognition in the Wild](https://mlsanity.com/paper/2308.16739/) (2023)
- [Learning Rich Features for Gait Recognition by Integrating Skeletons and Silhouettes](https://mlsanity.com/paper/2110.13408/) (2021)
- [Multi-Source Unsupervised Domain Adaptation via Pseudo Target Domain](https://mlsanity.com/paper/2202.10725/) (2022)
- [GaitGL: Learning Discriminative Global-Local Feature Representations for Gait Recognition](https://mlsanity.com/paper/2208.01380/) (2022)
- [Learning Gait Representation from Massive Unlabelled Walking Videos: A Benchmark](https://mlsanity.com/paper/2206.13964/) (2022)
- [xCos: An Explainable Cosine Metric for Face Verification Task](https://mlsanity.com/paper/2003.05383/) (2020)
- [Automated Latent Fingerprint Recognition](https://mlsanity.com/paper/1704.01925/) (2017)
- [Gradual Source Domain Expansion for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2311.09599/) (2023)
- [Silhouette based View embeddings for Gait Recognition under Multiple Views](https://mlsanity.com/paper/2108.05524/) (2021)
- [Face Recognition: A Novel Multi-Level Taxonomy based Survey](https://mlsanity.com/paper/1901.00713/) (2019)
- [TraND: Transferable Neighborhood Discovery for Unsupervised Cross-domain Gait Recognition](https://mlsanity.com/paper/2102.04621/) (2021)
- [TriGait: Aligning and Fusing Skeleton and Silhouette Gait Data via a Tri-Branch Network](https://mlsanity.com/paper/2308.13340/) (2023)
- [Confidence-guided Centroids for Unsupervised Person Re-Identification](https://mlsanity.com/paper/2211.11921/) (2022)
- [Unsupervised Gait Recognition with Selective Fusion](https://mlsanity.com/paper/2303.10772/) (2023)

## Abstract (arXiv)

Gait recognition is an emerging identification technology that distinguishes individuals at long distances by analyzing individual walking patterns. Traditional techniques rely heavily on large-scale labeled datasets, which incurs high costs and significant labeling challenges. Recently, researchers have explored unsupervised gait recognition with clustering-based unsupervised domain adaptation methods and achieved notable success. However, these methods directly use pseudo-label generated by clustering and neglect pseudolabel noise caused by domain differences, which affects the effect of the model training process. To mitigate these issues, we proposed a novel model called GaitDCCR, which aims to reduce the influence of noisy pseudo labels on clustering and model training. Our approach can be divided into two main stages: clustering and training stage. In the clustering stage, we propose Dynamic Cluster Parameters (DCP) and Dynamic Weight Centroids (DWC) to improve the efficiency of clustering and obtain reliable cluster centroids. In the training stage, we employ the classical teacher-student structure and propose Confidence-based Pseudo-label Refinement (CPR) and Contrastive Teacher Module (CTM) to encourage noisy samples to converge towards clusters containing their true identities. Extensive experiments on public gait datasets have demonstrated that our simple and effective method significantly enhances the performance of unsupervised gait recognition, laying the foundation for its application in the real-world. We will release the code at https://github.com/YanSun-github/GaitDCCR upon acceptance.