论文标题
使用镜像注意和比较排名和匹配的语义线检测
Semantic Line Detection Using Mirror Attention and Comparative Ranking and Matching
论文作者
论文摘要
本文提出了一种用于检测语义线的新型算法。我们开发了三个网络：具有镜像注意的检测网络（D-NET）以及比较排名和匹配网络（R-NET和M-NET）。 D-NET通过利用丰富的上下文信息来提取语义线。为此，我们设计了镜像注意模块。然后，通过对提取的语义线的成对比较，我们迭代地选择了最多的语义线，然后删除了冗余的线条与所选的语义线重叠。对于成对比较，我们在暹罗建筑中开发了R-NET和M-NET。实验表明，所提出的算法的表现明显优于常规语义线检测器。此外，我们将提出的算法成功地检测出两种重要的语义线：主要的平行线和反射对称轴。我们的代码可在https://github.com/dongkwonjin/semantic-line-drm上找到。
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.
