Deep High-Resolution Representation Learning for Human Pose Estimation論文解析
High-Resoultion Net(HRNet)由微軟亞洲研究院和中科大提出，發表在CVPR2019。 模型的主要特點是在整個過程中特徵圖（Feature Map）始終保持高分辨率，這與之前主流方法思路上有很大的不同。在HRNet之前，2D人體姿態估計算法是採用（Hourglass/CPN/Simple Baseline/MSPN等）將高分辨率特徵圖下采樣至低分辨率，再從低分辨率特徵圖恢復至高
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- 1. Deep High-Resolution Representation Learning for Human Pose Estimation
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- 4. 人體姿態識別~HRNet論文筆記~《Deep High-Resolution Representation Learning for Human Pose Estimation》
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- 8. (26) Distribution-Aware Coordinate Representation for Human Pose Estimation
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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. Deep High-Resolution Representation Learning for Human Pose Estimation論文翻譯閱讀 - 2019CVPR
- 4. 人體姿態識別~HRNet論文筆記~《Deep High-Resolution Representation Learning for Human Pose Estimation》
- 5. 《HigherHRNet：Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation》論文筆記
- 6. RePose: Learning Deep Kinematic Priors for Fast Human Pose Estimation
- 7. March 11-Deep High-Resolution Representation Learning for Human Pose Estimation-paper reading
- 8. (26) Distribution-Aware Coordinate Representation for Human Pose Estimation
- 9. Learning Feature Pyramids for Human Pose Estimation（理解）
- 10. Deep Learning Models and Code for Pose Estimation