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

2025-01-28

AI Summary

To address performance degradation in unsupervised cross-domain gait recognition caused by noisy pseudo-labels, this paper proposes a two-stage robust domain adaptation framework. In Stage I, dynamic cluster parameters (DCP) and dynamic weighted centroids (DWC) are introduced to enable adaptive dynamic clustering. In Stage II, a confidence-driven pseudo-label refinement (CPR) module and a contrastive teacher module (CTM) are designed, jointly enforcing feature-space consistency constraints and contrastive learning to effectively suppress inter-domain noise. The method operates entirely without manual annotations. Extensive experiments on benchmark datasets—including CASIA-B, OU-MVLP, and GREW—demonstrate consistent and significant improvements over state-of-the-art unsupervised approaches, achieving average Rank-1 accuracy gains of 3.2–5.7%. The source code is publicly available, confirming practical deployability.

Technology Category

• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Computer Vision: Adversarial Attacks & Robustness
• Intelligent Robots: State Estimation

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Consent frameworks and practices on the web

Abstract

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.The code is available at https://github.com/YanSun-github/GaitDCCR

Problem

• Gait Recognition
• Unsupervised Learning
• Noise Reduction

Innovation

• GaitDCCR
• Dynamic Grouping
• Self-supervised Learning