CVPR2019 | 15篇论文速递（涵盖目标检测、语义分割和姿态估计等方向）

导读】CVPR 2019 接收论文列表已经出来了，但只是一些索引号，所以并没有完整的论文合集。CVer 最近也在整理收集，今天一文涵盖15篇 CVPR 2019 论文速递，内容涵盖目标检测、语义分割和姿态估计等方向。

特别鸣谢 CV_arXiv_Daily 公众号提供的素材，本文介绍的论文已经同步至：

https://github.com/zhengzhugithub/CV-arXiv-Daily

姿态估计

[1] CVPR 2019 Pose estimation文章，目前SOTA,已经开源

论文题目：Deep High-Resolution Representation Learning for Human Pose Estimation

作者：Ke Sun, Bin Xiao, Dong Liu, Jingdong Wang

论文链接：https://arxiv.org/abs/1902.09212

代码链接：https://github.com/leoxiaobin/deep-high-resolution-net.pytorch

摘要: This is an official pytorch implementation of Deep High-Resolution Representation Learning for Human Pose Estimation. In this work, we are interested in the human pose estimation problem with a focus on learning reliable high-resolution representations. Most existing methods recover high-resolution representations from low-resolution representations produced by a high-to-low resolution network. Instead, our proposed network maintains high-resolution representations through the whole process. We start from a high-resolution subnetwork as the first stage, gradually add high-to-low resolution subnetworks one by one to form more stages, and connect the mutli-resolution subnetworks in parallel. We conduct repeated multi-scale fusions such that each of the high-to-low resolution representations receives information from other parallel representations over and over, leading to rich high-resolution representations. As a result, the predicted keypoint heatmap is potentially more accurate and spatially more precise. We empirically demonstrate the effectiveness of our network through the superior pose estimation results over two benchmark datasets: the COCO keypoint detection dataset and the MPII Human Pose dataset.

来自微软和中国科技大学研究学者的论文《Deep High-Resolution Representation Learning for Human Pose Estimation》和相应代码甫一公布，立刻引起大家的关注，不到一天之内，github上已有将近50颗星。

今天就跟大家一起来品读此文妙处。

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