Neural Architecture Search (NAS) technologies have been successfully performed for efficient neural architectures for tasks such as image classification and semantic segmentation. However, existing works implement NAS for target tasks independently of domain knowledge and focus only on searching for an architecture to replace the human-designed network in a common pipeline. Can we exploit human prior knowledge to guide NAS? To address it, we propose a framework, named Pose Neural Fabrics Search (PNFS), introducing prior knowledge of body structure into NAS for human pose estimation. We lead a new neural architecture search space, by parameterizing cell-based neural fabric, to learn micro as well as macro neural architecture using a differentiable search strategy. To take advantage of part-based structural knowledge of the human body and learning capability of NAS, global pose constraint relationships are modeled as multiple part representations, each of which is predicted by a personalized neural fabric. In part representation, we view human skeleton keypoints as entities by representing them as vectors at image locations, expecting it to capture keypoint's feature in a relaxed vector space. The experiments on MPII and MS-COCO datasets demonstrate that PNFS can achieve comparable performance to state-of-the-art methods, with fewer parameters and lower computational complexity.
翻译:已经成功地为图像分类和语义分解等任务成功应用了高效神经结构的神经结构,但是,现有的工程在与域知识无关的情况下执行NAS,以完成目标任务,而不受域知识的影响,只侧重于寻找一个建筑以取代共同管道中的人设计的网络。我们能否利用人类先前的知识来指导NAS?为了解决这个问题,我们提出了一个框架,名为Pose Neaural造型搜索(PNFS),在NAS中引入了先前的身体结构知识,以进行人造型估测。我们通过对基于细胞的神经结构进行参数化,领导一个新的神经结构搜索空间,以便利用一种不同的搜索战略学习微型和宏观神经结构。为了利用人体的局部结构知识和NAS的学习能力,全球的制约关系可建模成多个部分,每个部分都由个性化的神经结构来预测。我们把人类骨骼关键点视为实体,在图像位置上将它们作为矢量,期望它能够捕捉到一个较宽松的矢量空间中的关键点的特征特征。关于MPII和MS-CO-较低的计算参数的实验可以实现可比较的状态的功能变化的参数。