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.

翻译：在本文中,我们侧重于处理精确的临界点协调回归任务。大多数现有方法都采用具有大量参数的复杂网络,导致一个成本效率低的重模型。为了克服这一限制,我们开发了一个小型但又具有差异性的模型,称为STair网络,可以简单地堆叠到一个准确的多阶段构成估计系统上。具体地说,为了降低计算成本,STair网络由新的基本特征提取块组成,侧重于促进特征多样性,获得具有较少参数的丰富的当地代表,从而在效率和绩效方面实现令人满意的平衡。为了进一步改善绩效,我们引入了两个计算成本微不足道的机制,侧重于特征融合和补充。我们展示了STair网络在两个标准数据集上的效力,例如,1级STair网络在CO测试数据集上比HRNet高出5.5%,其参数少80 ⁇,GFLOPs少68%。