Abstract
Generalized gait recognition, which aims to achieve robust performance across diverse domains, remains a challenging problem due to severe domain shifts in viewpoints, appearances, and environments. While mixed-dataset training is widely used to enhance generalization, it introduces new obstacles including inter-dataset optimization conflicts and redundant or noisy samples, both of which hinder effective representation learning. To address these challenges, we propose a unified framework that systematically improves cross-domain gait recognition. First, we design a disentangled triplet loss that isolates supervision signals across datasets, mitigating gradient conflicts during optimization. Second, we introduce a targeted dataset distillation strategy that filters out the least informative 20\% of training samples based on feature redundancy and prediction uncertainty, enhancing data efficiency. Extensive experiments on CASIA-B, OU-MVLP, Gait3D, and GREW demonstrate that our method significantly improves cross-dataset recognition for both GaitBase and DeepGaitV2 backbones, without sacrificing source-domain accuracy. Code will be released at this https URL.
Abstract (translated)
通用步态识别旨在实现跨不同领域的稳健性能，但由于视角、外观和环境的严重领域偏移，这仍然是一个具有挑战性的问题。虽然混合数据集训练广泛用于增强泛化能力，但它引入了新的障碍，包括数据集间的优化冲突以及冗余或噪声样本，这些都阻碍了有效的表示学习。为了解决这些问题，我们提出了一种统一框架，系统地改进跨域步态识别性能。 首先，我们设计了一个解耦三元组损失函数，该函数将不同数据集之间的监督信号隔离开来，从而在优化过程中减轻梯度冲突。其次，我们引入了针对性的数据集蒸馏策略，根据特征冗余和预测不确定性筛选出训练样本中信息量最少的20%，以提高数据效率。 在CASIA-B、OU-MVLP、Gait3D和GREW上的大量实验表明，我们的方法显著提高了基于GaitBase和DeepGaitV2骨干网络的跨数据集识别性能，并且没有牺牲源域的准确性。代码将在此 [URL] 发布。
URL
https://arxiv.org/abs/2505.15176
