Recently, stacked hourglass network has shown outstanding performance in human pose estimation. However, repeated bottom-up and top-down stride convolution operations in deep convolutional neural networks lead to a significant decrease in the initial image resolution. In order to address this problem, we propose to incorporate affinage module and residual attention module into stacked hourglass network for human pose estimation. This paper introduces a novel network architecture to replace the stacked hourglass network of up-sampling operation for getting high-resolution features. We refer to the architecture as an affinage module which is critical to improve the performance of the stacked hourglass network. Additionally, we also propose a novel residual attention module to increase the supervision of up-sample process. The effectiveness of the introduced module is evaluated on standard benchmarks. Various experimental results demonstrated that our method can achieve more accurate and more robust human pose estimation results in images with complex background.

中文翻译：

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多路径亲和力堆叠—用于人体姿势估计的沙漏网络

近来，堆叠式沙漏网络在人体姿势估计中表现出出色的性能。但是，在深度卷积神经网络中重复执行自下而上和自上而下的跨步卷积操作会导致初始图像分辨率显着下降。为了解决这个问题，我们建议将亲和度模块和残差注意模块合并到堆叠的沙漏网络中，以进行人体姿势估计。本文介绍了一种新颖的网络体系结构，以取代向上采样操作的堆叠沙漏网络以获取高分辨率功能。我们将该架构称为关联模块，这对于提高堆叠式沙漏网络的性能至关重要。此外，我们还提出了一种新颖的剩余注意力模块，以增加对上采样过程的监督。引入的模块的有效性在标准基准上进行评估。各种实验结果表明，我们的方法可以在背景复杂的图像中获得更准确，更可靠的人体姿势估计结果。