  A novel algorithm to detect semantic lines is proposed in this paper. We
develop three networks: detection network with mirror attention (D-Net) and
comparative ranking and matching networks (R-Net and M-Net). D-Net extracts
semantic lines by exploiting rich contextual information. To this end, we
design the mirror attention module. Then, through pairwise comparisons of
extracted semantic lines, we iteratively select the most semantic line and
remove redundant ones overlapping with the selected one. For the pairwise
comparisons, we develop R-Net and M-Net in the Siamese architecture.
Experiments demonstrate that the proposed algorithm outperforms the
conventional semantic line detector significantly. Moreover, we apply the
proposed algorithm to detect two important kinds of semantic lines
successfully: dominant parallel lines and reflection symmetry axes. Our codes
are available at https://github.com/dongkwonjin/Semantic-Line-DRM.
