论文标题
部分多绘图匹配的宇宙点表示学习
Universe Points Representation Learning for Partial Multi-Graph Matching
论文作者
论文摘要
自然世界的许多挑战可以作为图形匹配问题进行表述。以前的基于深度学习的方法主要考虑完整的两圈匹配设置。在这项工作中，我们研究了具有多壁周期一致性保证的更普遍的部分匹配问题。在图形深度学习的最新进展的基础上，我们为部分多绘图匹配提出了一种新颖的数据驱动方法（URL），该方法使用对象到宇宙的表述并了解抽象宇宙点的潜在表示。提出的方法在语义关键点匹配问题中推进了最新技术，并在Pascal VOC，Cub和Willow数据集上进行了评估。此外，合成图匹配数据集上的一组受控实验集证明了我们的方法对大量节点及其对高偏差的鲁棒性的图表的可扩展性。
Many challenges from natural world can be formulated as a graph matching problem. Previous deep learning-based methods mainly consider a full two-graph matching setting. In this work, we study the more general partial matching problem with multi-graph cycle consistency guarantees. Building on a recent progress in deep learning on graphs, we propose a novel data-driven method (URL) for partial multi-graph matching, which uses an object-to-universe formulation and learns latent representations of abstract universe points. The proposed approach advances the state of the art in semantic keypoint matching problem, evaluated on Pascal VOC, CUB, and Willow datasets. Moreover, the set of controlled experiments on a synthetic graph matching dataset demonstrates the scalability of our method to graphs with large number of nodes and its robustness to high partiality.
