Conventional networks for object skeleton detection are usually hand-crafted. Despite the effectiveness, hand-crafted network architectures lack the theoretical basis and require intensive prior knowledge to implement representation complementarity for objects/parts in different granularity. In this paper, we propose an adaptive linear span network (AdaLSN), driven by neural architecture search (NAS), to automatically configure and integrate scale-aware features for object skeleton detection. AdaLSN is formulated with the theory of linear span, which provides one of the earliest explanations for multi-scale deep feature fusion. AdaLSN is materialized by defining a mixed unit-pyramid search space, which goes beyond many existing search spaces using unit-level or pyramid-level features. Within the mixed space, we apply genetic architecture search to jointly optimize unit-level operations and pyramid-level connections for adaptive feature space expansion. AdaLSN substantiates its versatility by achieving significantly higher accuracy and latency trade-off compared with the state-of-the-arts. It also demonstrates general applicability to image-to-mask tasks such as edge detection and road extraction. Code is available at https://github.com/sunsmarterjie/SDL-Skeletongithub.com/sunsmarterjie/SDL-Skeleton.

中文翻译：

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用于物体骨架检测的自适应线性跨度网络


用于物体骨架检测的传统网络通常是手工制作的。尽管有效，但手工制作的网络架构缺乏理论基础，并且需要大量的先验知识来实现​​不同粒度的对象/部分的表示互补。在本文中，我们提出了一种由神经架构搜索（NAS）驱动的自适应线性跨度网络（AdaLSN），用于自动配置和集成用于对象骨架检测的尺度感知特征。 AdaLSN是用线性跨度理论制定的，它为多尺度深度特征融合提供了最早的解释之一。 AdaLSN 通过定义混合单元金字塔搜索空间来具体化，该空间超越了许多使用单元级或金字塔级特征的现有搜索空间。在混合空间内，我们应用遗传架构搜索来联合优化单元级操作和金字塔级连接，以实现自适应特征空间扩展。与最先进的技术相比，AdaLSN 通过实现显着更高的准确性和延迟权衡来证实其多功能性。它还展示了对图像到掩模任务（例如边缘检测和道路提取）的普遍适用性。代码可在 https://github.com/sunsmarterjie/SDL-Skeletongithub.com/sunsmarterjie/SDL-Skeleton 获取。