Stacked Hourglass Networks
终于跳进人体姿态估计的坑了，本篇文章来源于ECCV2016。 论文链接 它的亮点是通过反复上采样和下采样，不断地融合空间信息，这样就可以把各个关键点的信息连贯地融合到一起，最后输出基于像素点的预测，它是一个singel pipeline，其中feature map最小的是4 * 4。 它的设计如图3：经由卷积层和max pooling层将feature map降至较低的分辨率，在Max pooli
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相关文章
- 1. Stacked Hourglass Networks简析
- 2. Stacked Hourglass Networks 理解
- 3. Stacked Hourglass Networks for Human Pose Estimation
- 4. Stacked Hourglass Network for Human Pose Estimation
- 5. Stacked Hourglass Networks - 堆叠沙漏网络结构详解
- 6. 论文翻译：Stacked Hourglass Networks for Human Pose Estimation
- 7. 论文笔记 Stacked Hourglass Networks for Human Pose Estimation
- 8. 论文解读-Stacked Hourglass Networks for Human Pose Estimation
- 9. 论文阅读理解 - Stacked Hourglass Networks for Human Pose Estimation
- 10. 姿态检测整理--01-Stacked Hourglass Networks for Human Pose Estimation