LKConvPose: A Pose Estimation Model with Large Receptive Field

Recently, significant progress has been made in 2D human pose estimation. While some research has focused on enhancing the accuracy of keypoint detection, others have aimed at reducing model size. However, most models excel in either one aspect or the other, but rarely both simultaneously. In this paper, we address the challenge of balancing accuracy and inference speed. Inspired by large-kernel convolutions and attention mechanisms, we introduce LKConvPose, a hybrid CNN architecture that achieves high keypoint detection accuracy with low computational cost. Specifically, LKConvPose-S attains 76.5 AP on the COCO validation dataset using only 4 GFLOPs, making it the most efficient model at its scale.