A high-resolution network exhibits remarkable capability in extracting
multi-scale features for human pose estimation, but fails to capture long-range
interactions between joints and has high computational complexity. To address
these problems, we present a Dynamic lightweight High-Resolution Network
(Dite-HRNet), which can efficiently extract multi-scale contextual information
and model long-range spatial dependency for human pose estimation.
Specifically, we propose two methods, dynamic split convolution and adaptive
context modeling, and embed them into two novel lightweight blocks, which are
named dynamic multi-scale context block and dynamic global context block. These
two blocks, as the basic component units of our Dite-HRNet, are specially
designed for the high-resolution networks to make full use of the parallel
multi-resolution architecture. Experimental results show that the proposed
network achieves superior performance on both COCO and MPII human pose
estimation datasets, surpassing the state-of-the-art lightweight networks. Code
is available at: this https URL

通过在高分辨率网络中引入动态轻量化、多尺度上下文信息提取、长距离空间依赖建模的方法，本文提出了一种动态轻量化高分辨率网络模型（Dite-HRNet），该模型在 COCO 和 MPII 人体姿态估计数据集上取得了优异的表现，超越了现有的轻量化网络模型。