Context-Aware Human Pose Estimation via Hierarchical Information Arbitration

Human pose estimation requires accurate localization of body keypoints under complex backgrounds, occlusion, and diverse human postures. Existing high-resolution pose-estimation networks preserve spatial details effectively, but their static information flow limits their adaptability to different image contexts. To address this limitation, this paper proposes a context-aware hierarchical information arbitration method that dynamically regulates feature interaction at both multi-resolution fusion and residual feature refinement levels. The proposed method achieves superior performance on COCO, reaching 77.0 average precision and improving the High-Resolution Network baseline by 3.6 percentage points, with only a minor increase in model parameters. These results demonstrate that adaptive information arbitration improves pose-estimation accuracy and robustness while maintaining computational efficiency.