Multi-Context Attention for Human Pose Estimation
1、本文的三个创新点 Multi-context attention，CRF，HRU的设计增大感受野 2、首先作者指出多语义表示的重要性，attention map依托于feature map集中在图像中感兴趣的部分，他能帮助恢复遗失的身体部件，并且能够区分出有歧义的背景。作者采用stacked hourglass作为依附 1）每个hourglass从不同分辨率的feature map来产生att
相关文章
- 1. 《Multi-Context Attention for Human Pose Estimation》
- 2. Stacked Hourglass Networks for Human Pose Estimation
- 3. Stacked Hourglass Network for Human Pose Estimation
- 4. Cascade Feature Aggregation for Human Pose Estimation
- 5. Fast Human Pose Estimation
- 6. 3D Human Pose Estimation = 2D Pose Estimation + Matching(2017)
- 7. (20) Recurrent Human Pose Estimation
- 8. 论文阅读理解 - Multi-Context Attention for Human Pose Estimation
- 9. 姿态论文整理--03-Multi-Context Attention for human pose estimation
- 10. (26) Distribution-Aware Coordinate Representation for Human Pose Estimation
- 更多相关文章...
- • Swift for 循环 - Swift 教程
- • Scala for循环 - Scala教程
- • PHP开发工具
- • C# 中 foreach 遍历的用法
相关标签/搜索
每日一句
-
每一个你不满意的现在，都有一个你没有努力的曾经。
欢迎关注本站公众号,获取更多信息
相关文章
- 1. 《Multi-Context Attention for Human Pose Estimation》
- 2. Stacked Hourglass Networks for Human Pose Estimation
- 3. Stacked Hourglass Network for Human Pose Estimation
- 4. Cascade Feature Aggregation for Human Pose Estimation
- 5. Fast Human Pose Estimation
- 6. 3D Human Pose Estimation = 2D Pose Estimation + Matching(2017)
- 7. (20) Recurrent Human Pose Estimation
- 8. 论文阅读理解 - Multi-Context Attention for Human Pose Estimation
- 9. 姿态论文整理--03-Multi-Context Attention for human pose estimation
- 10. (26) Distribution-Aware Coordinate Representation for Human Pose Estimation