Abstract
Current gait recognition methodologies generally necessitate retraining when encountering new datasets. Nevertheless, retrained models frequently encounter difficulties in preserving knowledge from previous datasets, leading to a significant decline in performance on earlier test sets. To tackle these challenges, we present a continual gait recognition task, termed GaitAdapt, which supports the progressive enhancement of gait recognition capabilities over time and is systematically categorized according to various evaluation scenarios. Additionally, we propose GaitAdapter, a non-replay continual learning approach for gait recognition. This approach integrates the GaitPartition Adaptive Knowledge (GPAK) module, employing graph neural networks to aggregate common gait patterns from current data into a repository constructed from graph vectors. Subsequently, this repository is used to improve the discriminability of gait features in new tasks, thereby enhancing the model's ability to effectively recognize gait patterns. We also introduce a Euclidean Distance Stability Method (EDSN) based on negative pairs, which ensures that newly added gait samples from different classes maintain similar relative spatial distributions across both previous and current gait tasks, thereby alleviating the impact of task changes on the distinguishability of original domain features. Extensive evaluations demonstrate that GaitAdapter effectively retains gait knowledge acquired from diverse tasks, exhibiting markedly superior discriminative capability compared to alternative methods.
Abstract (translated)
当前的步态识别方法在遇到新的数据集时通常需要重新训练。然而，重新训练后的模型往往难以保持之前数据集中学到的知识，导致早期测试集上的性能显著下降。为了解决这些问题，我们提出了一项连续步态识别任务，称为GaitAdapt，该任务支持步态识别能力的逐步增强，并根据各种评估场景系统地进行分类。 此外，我们提出了一个名为GaitAdapter的方法，这是一种非重放持续学习方法，专门用于步态识别。此方法整合了GaitPartition自适应知识（GPAK）模块，利用图神经网络将当前数据中的通用步态模式聚合到由图向量构建的仓库中。随后，该仓库被用来提升新任务中步态特征的区别能力，从而增强模型有效识别步态模式的能力。 我们还引入了一种基于负样本对的欧氏距离稳定性方法（EDSN），以确保来自不同类别的新增加的步态样本在先前和当前的步态任务之间保持相似的空间分布关系。这样可以减轻任务变化对原始领域特征区分能力的影响。 广泛的评估表明，GaitAdapter能够有效保留从各种任务中获得的步态知识，并且与替代方法相比，在区别能力方面表现出显著的优势。
URL
https://arxiv.org/abs/2508.03375
