As multi-scale features are necessary for human pose estimation tasks, high-resolution networks are widely applied. To improve efficiency, lightweight modules are proposed to replace costly point-wise convolutions in high-resolution networks, including channel weighting and spatial weighting methods. However, they fail to maintain the consistency of weights and capture global spatial information. To address these problems, we present a Grouped lightweight High-Resolution Network (Greit-HRNet), in which we propose a Greit block including a group method Grouped Channel Weighting (GCW) and a spatial weighting method Global Spatial Weighting (GSW). GCW modules group conditional channel weighting to make weights stable and maintain the high-resolution features with the deepening of the network, while GSW modules effectively extract global spatial information and exchange information across channels. In addition, we apply the Large Kernel Attention (LKA) method to improve the whole efficiency of our Greit-HRNet. Our experiments on both MS-COCO and MPII human pose estimation datasets demonstrate the superior performance of our Greit-HRNet, outperforming other state-of-the-art lightweight networks.

翻译：由于人体姿态估计任务需要多尺度特征，高分辨率网络被广泛应用。为提高效率，研究者提出了轻量化模块以替代高分辨率网络中代价高昂的逐点卷积，包括通道加权和空间加权方法。然而，这些方法难以保持权重一致性并捕获全局空间信息。为解决这些问题，我们提出了一种分组轻量化高分辨率网络（Greit-HRNet），其中设计了包含分组方法——分组通道加权（GCW）与空间加权方法——全局空间加权（GSW）的Greit模块。GCW模块通过分组条件通道加权使权重保持稳定，并在网络加深过程中维持高分辨率特征；而GSW模块能有效提取全局空间信息并实现跨通道信息交互。此外，我们采用大核注意力（LKA）方法以提升Greit-HRNet的整体效率。在MS-COCO和MPII人体姿态估计数据集上的实验表明，我们的Greit-HRNet具有优越性能，超越了其他先进的轻量化网络。