论文标题
LayoutGMN：结构布局相似性的神经图匹配
LayoutGMN: Neural Graph Matching for Structural Layout Similarity
论文作者
论文摘要
我们提出了一个深层神经网络，以利用图形匹配网络（GMN）来预测2D布局之间的结构相似性。我们的网络创建的LayoutGMN使用神经图匹配来了解布局度量，并使用在三重态网络设置下设计的基于注意力的GMN。为了训练我们的网络，我们利用了通过像素相交（IOUS）获得的弱标记来定义三重态损失。重要的是，LayoutGMN具有结构性偏见，可以有效地弥补缺乏结构意识。我们通过大规模数据集的检索实验在两种突出的布局，即平面图和UI设计上进行了证明。特别是，与IOUS和其他基线相比，我们网络的检索结果更好地匹配了人类对结构布局相似性的判断，包括基于图神经网络和图像卷积的最新方法。此外，LayoutGMN是第一个提供结构布局相似性和布局元素之间结构匹配的公制学习的深层模型。
We present a deep neural network to predict structural similarity between 2D layouts by leveraging Graph Matching Networks (GMN). Our network, coined LayoutGMN, learns the layout metric via neural graph matching, using an attention-based GMN designed under a triplet network setting. To train our network, we utilize weak labels obtained by pixel-wise Intersection-over-Union (IoUs) to define the triplet loss. Importantly, LayoutGMN is built with a structural bias which can effectively compensate for the lack of structure awareness in IoUs. We demonstrate this on two prominent forms of layouts, viz., floorplans and UI designs, via retrieval experiments on large-scale datasets. In particular, retrieval results by our network better match human judgement of structural layout similarity compared to both IoUs and other baselines including a state-of-the-art method based on graph neural networks and image convolution. In addition, LayoutGMN is the first deep model to offer both metric learning of structural layout similarity and structural matching between layout elements.
