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 this https URL

本文提出了一种新算法来检测语义线。通过利用富含上下文信息的镜像注意力模块，从三个网络（具有镜像注意力的检测网络 D-Net 和比较排序匹配网络 R-Net 和 M-Net）中提取语义线，然后通过成对比较迭代选择最具语义性的语义线并移除与所选线重叠的冗余线，证明了该算法显著优于传统语义线检测器，并成功应用于检测主要平行线和反射对称轴。