# Global Context for Convolutional Pose Machines

arXiv: https://arxiv.org/abs/1906.04104 · HTML: https://mlsanity.com/paper/1906.04104/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jun 2019.

## Built on by (most cited first, 1 of 1)

- [On Exploring Pose Estimation as an Auxiliary Learning Task for Visible-Infrared Person Re-identification](https://mlsanity.com/paper/2201.03859/) (2022)

## Abstract (arXiv)

Convolutional Pose Machine is a popular neural network architecture for articulated pose estimation. In this work we explore its empirical receptive field and realize, that it can be enhanced with integration of a global context. To do so U-shaped context module is proposed and compared with the pyramid pooling and atrous spatial pyramid pooling modules, which are often used in semantic segmentation domain. The proposed neural network achieves state-of-the-art accuracy with 87.9% PCKh for single-person pose estimation on the Look Into Person dataset. A smaller version of this network runs more than 160 frames per second while being just 2.9% less accurate. Generalization of the proposed approach is tested on the MPII benchmark and shown, that it faster than hourglass-based networks, while provides similar accuracy. The code is available at https://github.com/opencv/openvino_training_extensions/tree/develop/pytorch_toolkit/human_pose_estimation .