ScaleFormer architecture for scale invariant human pose estimation with enhanced mixed features.

ABSTRACT: Human pose estimation is a fundamental task in computer vision. However, existing methods face performance fluctuation challenges when processing human targets at different scales, especially in outdoor scenes where target distances and viewing angles frequently change. This paper proposes ScaleFormer, a novel scale-invariant pose estimation framework that effectively addresses multi-scale pose estimation problems by innovatively combining the hierarchical feature extraction capabilities of Swin Transformer with the fine-grained feature enhancement mechanisms of ConvNeXt. We design an adaptive feature representation mechanism that enables the model to maintain consistent performance across different scales. Extensive experiments on the MPII human pose dataset demonstrate that ScaleFormer s

SUBMITTER: Ge C 

PROVIDER: S-EPMC12311106 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature