# Towards Simple and Accurate Human Pose Estimation with Stair Network Source: [Towards Simple and Accurate Human Pose Estimation with Stair Network]() Collection index: [llms.txt]() Data updated: 2025-10-26T00:47:11.173Z Authors: Chenru Jiang, Kaizhu Huang, Shufei Zhang, Shufei Zhang, Jimin Xiao, Zhenxing Niu, Amir Hussain Published: 2022-02-18T10:37:13.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()