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

• URL: http://arxiv.org/abs/2505.18132v3
• Date: Tue, 17 Jun 2025 09:32:43 GMT
• Title: BiggerGait: Unlocking Gait Recognition with Layer-wise Representations from Large Vision Models
• Authors: Dingqiang Ye, Chao Fan, Zhanbo Huang, Chengwen Luo, Jianqiang Li, Shiqi Yu, Xiaoming Liu,
• Abstract summary: This work investigates the impact of layer-wise representations on downstream recognition tasks.<n>We propose a simple and universal baseline for LVM-based gait recognition, termed BiggerGait.<n> Comprehensive evaluations on CCPG, CAISA-B*, SUSTech1K, and CCGR_MINI validate the superiority of BiggerGait across both within- and cross-domain tasks.
• Score: 16.21103558769559
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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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