We present AutoPose, a novel neural architecture search(NAS) framework that is capable of automatically discovering multiple parallel branches of cross-scale connections towards accurate and high-resolution 2D human pose estimation. Recently, high-performance hand-crafted convolutional networks for pose estimation show growing demands on multi-scale fusion and high-resolution representations. However, current NAS works exhibit limited flexibility on scale searching, they dominantly adopt simplified search spaces of single-branch architectures. Such simplification limits the fusion of information at different scales and fails to maintain high-resolution representations. The presentedAutoPose framework is able to search for multi-branch scales and network depth, in addition to the cell-level microstructure. Motivated by the search space, a novel bi-level optimization method is presented, where the network-level architecture is searched via reinforcement learning, and the cell-level search is conducted by the gradient-based method. Within 2.5 GPU days, AutoPose is able to find very competitive architectures on the MS COCO dataset, that are also transferable to the MPII dataset. Our code is available at https://github.com/VITA-Group/AutoPose.

AutoPose是一种新颖的神经架构搜索（NAS）框架，它能够自动发现多个跨尺度连接的平行分支，以实现准确和高分辨率的2D人体姿态估计，通过搜索多分支尺度和网络深度，以及在细胞水平进行搜寻的方法，它在短短2.5 GPU天内，在MS COCO数据集上找到了竞争力非常强的架构，可传递到MPII数据集。

AutoPose: 多尺度分支聚合姿态估计搜索