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 this https URL

本文提出了用金字塔残差模块来增强深度卷积神经网络的规模不变性，结合多分支网络设计权重初始化方法来在人体姿态估计任务上取得最新的最优结果。

学习特征金字塔以进行人体姿势估计

Learning Feature Pyramids for Human Pose Estimation

In this work, we propose a novel and efficient method for articulated human
pose estimation in videos using a convolutional network architecture, which
incorporates both color and motion features. We propose a new human body pose
dataset, FLIC-motion, that extends the FLIC dataset with additional motion
features. We apply our architecture to this dataset and report significantly
better performance than current state-of-the-art pose detection systems.

本研究提出了一种新颖高效的卷积神经网络结构，其中融合了颜色和运动特征，可用于视频中的人类姿态估计。同时，作者还创建了包含运动特征的新数据集 FLIC-motion，并对其应用了该网络结构，结果表明其性能显著优于当前最先进的姿态检测系统。