  Skeleton extraction is a task focused on providing a simple representation of
an object by extracting the skeleton from the given binary or RGB image. In
recent years many attractive works in skeleton extraction have been made. But
as far as we know, there is little research on how to utilize the context
information in the binary shape of objects. In this paper, we propose an
attention-based model called Context Attention Network (CANet), which
integrates the context extraction module in a UNet architecture and can
effectively improve the ability of network to extract the skeleton pixels.
Meanwhile, we also use some novel techniques including distance transform,
weight focal loss to achieve good results on the given dataset. Finally,
without model ensemble and with only 80% of the training images, our method
achieves 0.822 F1 score during the development phase and 0.8507 F1 score during
the final phase of the Pixel SkelNetOn Competition, ranking 1st place on the
leaderboard.
