SAGE-Yoga: Multi-Cue Learning for Yoga Pose Classification and Joint-Level Correction
Read the original on arXiv Computer Vision →SAGE-Yoga is a unified coarse‑to‑fine framework that performs yoga pose classification and joint‑level correction from a single RGB image. It uses a bagging ensemble of complementary visual backbones to rank candidate pose classes, a margin‑based gating mechanism to selectively apply geometric verification, and a medoid reference pose to compare joint angles against class‑specific distributions. On the Yoga‑82 dataset, the ensemble achieves 89.0% Top‑1 accuracy, while the full system reaches 90.7% Top‑1 accuracy and 90.1% Macro‑F1, demonstrating improved fine‑grained classification and interpretable corrective feedback.
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