TASK	DATASET	MODEL	METRIC NAME	METRIC VALUE	GLOBAL RANK	REMOVE
Pose Estimation	Leeds Sports Poses	Pyramid Residual Modules (PRMs)	PCK	93.9%	# 6
Pose Estimation	MPII Human Pose	Pyramid Residual Modules (PRMs)	PCKh-0.5	92.0	# 14

Badge	Markdown
	`[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/learning-feature-pyramids-for-human-pose/pose-estimation-on-leeds-sports-poses)](https://paperswithcode.com/sota/pose-estimation-on-leeds-sports-poses?p=learning-feature-pyramids-for-human-pose)`
	`[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/learning-feature-pyramids-for-human-pose/pose-estimation-on-mpii-human-pose)](https://paperswithcode.com/sota/pose-estimation-on-mpii-human-pose?p=learning-feature-pyramids-for-human-pose)`

Learning Feature Pyramids for Human Pose Estimation

ICCV 2017 · Wei Yang, Shuang Li, Wanli Ouyang, Hongsheng Li, Xiaogang Wang ·

Articulated human pose estimation is a fundamental yet challenging task in computer vision. The difficulty is particularly pronounced in scale variations of human body parts when camera view changes or severe foreshortening happens. Although pyramid methods are widely used to handle scale changes at inference time, learning feature pyramids in deep convolutional neural networks (DCNNs) is still not well explored. In this work, we design a Pyramid Residual Module (PRMs) to enhance the invariance in scales of DCNNs. Given input features, the PRMs learn convolutional filters on various scales of input features, which are obtained with different subsampling ratios in a multi-branch network. Moreover, we observe that it is inappropriate to adopt existing methods to initialize the weights of multi-branch networks, which achieve superior performance than plain networks in many tasks recently. Therefore, we provide theoretic derivation to extend the current weight initialization scheme to multi-branch network structures. We investigate our method on two standard benchmarks for human pose estimation. Our approach obtains state-of-the-art results on both benchmarks. Code is available at https://github.com/bearpaw/PyraNet.