Human pose estimation - the process of recognizing human keypoints in a given image - is one of the most important tasks in computer vision and has a wide range of applications including movement diagnostics, surveillance, or self-driving vehicle. The accuracy of human keypoint prediction is increasingly improved thanks to the burgeoning development of deep learning. Most existing methods solved human pose estimation by generating heatmaps in which the ith heatmap indicates the location confidence of the ith keypoint. In this paper, we introduce novel network structures referred to as multiresolution representation learning for human keypoint prediction. At different resolutions in the learning process, our networks branch off and use extra layers to learn heatmap generation. We firstly consider the architectures for generating the multiresolution heatmaps after obtaining the lowest-resolution feature maps. Our second approach allows learning during the process of feature extraction in which the heatmaps are generated at each resolution of the feature extractor. The first and second approaches are referred to as multi-resolution heatmap learning and multi-resolution feature map learning respectively. Our architectures are simple yet effective, achieving good performance. We conducted experiments on two common benchmarks for human pose estimation: MS-COCO and MPII dataset.



表示学习是通过利用训练数据来学习得到向量表示,这可以克服人工方法的局限性。 表示学习通常可分为两大类,无监督和有监督表示学习。大多数无监督表示学习方法利用自动编码器(如去噪自动编码器和稀疏自动编码器等)中的隐变量作为表示。 目前出现的变分自动编码器能够更好的容忍噪声和异常值。 然而,推断给定数据的潜在结构几乎是不可能的。 目前有一些近似推断的策略。 此外,一些无监督表示学习方法旨在近似某种特定的相似性度量。提出了一种无监督的相似性保持表示学习框架,该框架使用矩阵分解来保持成对的DTW相似性。 通过学习保持DTW的shaplets,即在转换后的空间中的欧式距离近似原始数据的真实DTW距离。有监督表示学习方法可以利用数据的标签信息,更好地捕获数据的语义结构。 孪生网络和三元组网络是目前两种比较流行的模型,它们的目标是最大化类别之间的距离并最小化了类别内部的距离。