Abstract
Large vision models (LVM) based gait recognition has achieved impressive performance. However, existing LVM-based approaches may overemphasize gait priors while neglecting the intrinsic value of LVM itself, particularly the rich, distinct representations across its multi-layers. To adequately unlock LVM's potential, this work investigates the impact of layer-wise representations on downstream recognition tasks. Our analysis reveals that LVM's intermediate layers offer complementary properties across tasks, integrating them yields an impressive improvement even without rich well-designed gait priors. Building on this insight, we propose a simple and universal baseline for LVM-based gait recognition, termed BiggerGait. Comprehensive evaluations on CCPG, CAISA-B*, SUSTech1K, and CCGR\_MINI validate the superiority of BiggerGait across both within- and cross-domain tasks, establishing it as a simple yet practical baseline for gait representation learning. All the models and code will be publicly available.
Abstract (translated)
基于大型视觉模型（Large Vision Model，LVM）的步态识别已经取得了显著的成绩。然而，现有的基于LVM的方法可能过于强调步态先验知识，而忽略了LVM本身内在的价值，特别是其多层结构中的丰富且独特的表示能力。为了充分挖掘LVM的潜力，本研究探讨了逐层表示在下游任务中对步态识别的影响。我们的分析表明，LVM的中间层提供了跨任务互补的特性，即使没有丰富的精心设计的步态先验知识，将它们融合也能带来显著的改进。基于这一见解，我们提出了一种简单而通用的基础模型，用于LVM基的步态识别，并将其命名为BiggerGait。 在CCPG、CAISA-B*、SUSTech1K和CCGR_MINI等多个数据集上进行了全面评估，验证了BiggerGait在跨领域任务中的优越性，确立其为步态表示学习的一个简单而实用的基础模型。所有模型及其代码将在公开平台上提供。
URL
https://arxiv.org/abs/2505.18132
