Towards Simple and Accurate Human Pose Estimation with Stair Network
This addresses the need for cost-effective and accurate human pose estimation models in computer vision applications, representing an incremental improvement over existing methods.
The paper tackles the problem of precise keypoint coordinates regression in human pose estimation by developing a small and efficient model called STair Network, which achieves higher accuracy than HRNet by 5.5% on the COCO test dataset with 80% fewer parameters and 68% fewer GFLOPs.
In this paper, we focus on tackling the precise keypoint coordinates regression task. Most existing approaches adopt complicated networks with a large number of parameters, leading to a heavy model with poor cost-effectiveness in practice. To overcome this limitation, we develop a small yet discrimicative model called STair Network, which can be simply stacked towards an accurate multi-stage pose estimation system. Specifically, to reduce computational cost, STair Network is composed of novel basic feature extraction blocks which focus on promoting feature diversity and obtaining rich local representations with fewer parameters, enabling a satisfactory balance on efficiency and performance. To further improve the performance, we introduce two mechanisms with negligible computational cost, focusing on feature fusion and replenish. We demonstrate the effectiveness of the STair Network on two standard datasets, e.g., 1-stage STair Network achieves a higher accuracy than HRNet by 5.5% on COCO test dataset with 80\% fewer parameters and 68% fewer GFLOPs.