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arXiv · 2505.18132

BiggerGait: Unlocking Gait Recognition with Layer-wise Representations from Large Vision Models

Dingqiang Ye·Chao Fan·Zhanbo Huang·Chengwen Luo·Jianqiang Li·Shiqi Yu·Xiaoming Liu
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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.

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Dingqiang Ye, Chao Fan, Zhanbo Huang, Chengwen Luo, Jianqiang Li, Shiqi Yu, Xiaoming Liu. 2025-05-23. BiggerGait: Unlocking Gait Recognition with Layer-wise Representations from Large Vision Models. https://arxiv.org/abs/2505.18132

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