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Deep High-Resolution Representation Learning for Human Pose Estimation
時間 2020-12-29
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概述 這篇論文是中國科技大學和微軟亞洲研究院的論文,被 CVPR2019接收。 Motivation。 已經存在的大多數方法都是從低分辨率恢復高分辨率,相反,本文一直保持高分辨率,這樣預測的heatmap更加精確,預測的關節點更加準確。 網絡框架 網絡從一個高分辨率子網開始,逐步添加高到低分辨率子網來形成更多階段,並且並行連接多個分辨率子網,然後執行多尺度融合。 網絡細節 以輸入256x192爲例
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相關文章
1.
Deep High-Resolution Representation Learning for Human Pose Estimation
2.
Deep High-Resolution Representation Learning for Human Pose Estimation論文翻譯
3.
RePose: Learning Deep Kinematic Priors for Fast Human Pose Estimation
4.
March 11-Deep High-Resolution Representation Learning for Human Pose Estimation-paper reading
5.
Deep High-Resolution Representation Learning for Human Pose Estimation論文翻譯閱讀 - 2019CVPR
6.
Deep High-Resolution Representation Learning for Human Pose Estimation論文解析
7.
人體姿態識別~HRNet論文筆記~《Deep High-Resolution Representation Learning for Human Pose Estimation》
8.
(26) Distribution-Aware Coordinate Representation for Human Pose Estimation
9.
Deep Learning Models and Code for Pose Estimation
10.
姿態估計——HigherHRNet:Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation
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