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

Keywords: Gait Recognition, Large Vision Models

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 are available at https://github.com/ShiqiYu/OpenGait/.

Primary Area: Applications (e.g., vision, language, speech and audio, Creative AI)

Submission Number: 5223