  Conventional networks for object skeleton detection are usually hand-crafted.
Although effective, they require intensive priori knowledge to configure
representative features for objects in different scale granularity.In this
paper, we propose 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
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
\href{https://github.com/sunsmarterjie/SDL-Skeleton}{\color{magenta}github.com/sunsmarterjie/SDL-Skeleton}.
