  Detecting object skeletons in natural images presents challenging, due to
varied object scales, the complexity of backgrounds and various noises. The
skeleton is a highly compressing shape representation, which can bring some
essential advantages but cause the difficulties of detection. This skeleton
line occupies a rare proportion of an image and is overly sensitive to spatial
position. Inspired by these issues, we propose the ProMask, which is a novel
skeleton detection model. The ProMask includes the probability mask and vector
router. The skeleton probability mask representation explicitly encodes
skeletons with segmentation signals, which can provide more supervised
information to learn and pay more attention to ground-truth skeleton pixels.
Moreover, the vector router module possesses two sets of orthogonal basis
vectors in a two-dimensional space, which can dynamically adjust the predicted
skeleton position. We evaluate our method on the well-known skeleton datasets,
realizing the better performance than state-of-the-art approaches. Especially,
ProMask significantly outperforms the competitive DeepFlux by 6.2% on the
challenging SYM-PASCAL dataset. We consider that our proposed skeleton
probability mask could serve as a solid baseline for future skeleton detection,
since it is very effective and it requires about 10 lines of code.
