Human pose estimation is a fundamental but challenging task in computer vision. The estimation of human pose mainly depends on the global information of the keypoint type and the local information of the keypoint location. However, the consistency of the cascading process makes it difficult for each stacking network to form a differentiation and collaboration mechanism. In order to solve these problems, this paper introduces a new human pose estimation framework called Multi-Scale Collaborative (MSC) network. The pre-processing network forms feature maps of different sizes, and dispatches them to various locations of the stack network, with small-scale features reaching the front-end stacking network and large-scale features reaching the back-end stacking network. A new loss function is proposed for MSC network. Different keypoints have different weight coefficients of loss function at different scales, and the keypoint weight coefficients are dynamically adjusted from the top hourglass network to the bottom hourglass network. Experimental results show that the proposed method is competitive in MPII and LSP challenge leaderboard among the state-of-the-art methods.

中文翻译：

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用于人体姿态估计的多尺度协作网络

人体姿态估计是计算机视觉中一项基本但具有挑战性的任务。人体姿态估计主要依赖于关键点类型的全局信息和关键点位置的局部信息。但是，级联过程的一致性使得各个堆叠网络难以形成差异化协作机制。为了解决这些问题，本文引入了一种新的人体姿态估计框架，称为多尺度协作（MSC）网络。预处理网络形成不同大小的特征图，并将它们分派到堆栈网络的各个位置，小尺度特征到达前端堆叠网络，大尺度特征到达后端堆叠网络。为MSC网络提出了一种新的损失函数。不同关键点在不同尺度下损失函数的权重系数不同，关键点权重系数从顶部沙漏网络到底部沙漏网络动态调整。实验结果表明，在最先进的方法中，所提出的方法在 MPII 和 LSP 挑战排行榜中具有竞争力。