BiggerGait: Unlocking Gait Recognition with Layer-wise Representations from Large Vision Models
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2025
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866918061021331456 |
|---|---|
| author | Ye, Dingqiang Fan, Chao Huang, Zhanbo Luo, Chengwen Li, Jianqiang Yu, Shiqi Liu, Xiaoming |
| author_facet | Ye, Dingqiang Fan, Chao Huang, Zhanbo Luo, Chengwen Li, Jianqiang Yu, Shiqi Liu, Xiaoming |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_18132 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | BiggerGait: Unlocking Gait Recognition with Layer-wise Representations from Large Vision Models Ye, Dingqiang Fan, Chao Huang, Zhanbo Luo, Chengwen Li, Jianqiang Yu, Shiqi Liu, Xiaoming Computer Vision and Pattern Recognition 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. |
| title | BiggerGait: Unlocking Gait Recognition with Layer-wise Representations from Large Vision Models |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2505.18132 |