论文标题
通过软门控连接进行快速准确的人类姿势估算
Toward fast and accurate human pose estimation via soft-gated skip connections
论文作者
论文摘要
本文基于高度准确且高效的人姿势估计。基于完全卷积网络（FCN）的最新著作在这一困难问题上表现出了很好的结果。尽管事实证明，FCN内的残留连接对于达到高精度是典型的，但在提高最新艺术的准确性和效率的背景下，我们重新分析了这种设计选择。特别是，我们做出以下贡献：（a）我们提出了使用可学习参数的封闭式跳过连接，以控制宏模块中模块中每个通道的数据流。 （b）我们介绍了一个混合网络，该网络结合了沙漏和U-NET体系结构，该网络可以最大程度地减少网络中的身份连接数量，并提高相同参数预算的性能。我们的模型在MPII和LSP数据集上实现了最新的结果。此外，与原始的沙漏网络相比，由于模型大小和复杂性的减少3倍，我们的性能没有下降。
This paper is on highly accurate and highly efficient human pose estimation. Recent works based on Fully Convolutional Networks (FCNs) have demonstrated excellent results for this difficult problem. While residual connections within FCNs have proved to be quintessential for achieving high accuracy, we re-analyze this design choice in the context of improving both the accuracy and the efficiency over the state-of-the-art. In particular, we make the following contributions: (a) We propose gated skip connections with per-channel learnable parameters to control the data flow for each channel within the module within the macro-module. (b) We introduce a hybrid network that combines the HourGlass and U-Net architectures which minimizes the number of identity connections within the network and increases the performance for the same parameter budget. Our model achieves state-of-the-art results on the MPII and LSP datasets. In addition, with a reduction of 3x in model size and complexity, we show no decrease in performance when compared to the original HourGlass network.
