用于步态识别的基于动态聚类与对比精炼的无监督域适应
Unsupervised Domain Adaptation with Dynamic Clustering and Contrastive Refinement for Gait Recognition
AI总结：
提出GaitDCCR模型，通过动态聚类参数与权重质心优化聚类，并结合基于置信度的伪标签精炼与对比教师模块减少噪声伪标签影响，显著提升无监督步态识别性能。
摘要：
步态识别是一种新兴的识别技术，通过分析个人行走模式在远距离下区分个体。传统技术严重依赖大规模标注数据集，这导致了高昂的成本和巨大的标注挑战。近期，研究人员探索了基于聚类的无监督域适应方法的无监督步态识别，并取得了显著成功。然而，这些方法直接使用聚类生成的伪标签，忽略了由域差异引起的伪标签噪声，这影响了模型训练过程的效果。为了缓解这些问题，我们提出了一种名为 GaitDCCR 的新模型，旨在减少噪声伪标签对聚类和模型训练的影响。我们的方法可分为两个主要阶段：聚类和训练阶段。在聚类阶段，我们提出了动态聚类参数（DCP）和动态权重质心（DWC）以提高聚类效率并获取可靠的聚类质心。在训练阶段，我们采用经典的师生结构，并提出了基于置信度的伪标签精炼（CPR）和对比教师模块（CTM），以鼓励噪声样本向包含其真实身份的聚类收敛。在公开步态数据集上的大量实验表明，我们简单有效的方法显著提升了无监督步态识别的性能，为其在现实世界中的应用奠定了基础。代码将在被接受后发布于 https://github.com/YanSun-github/GaitDCCR 。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
