论文标题
空间SIM：使用图神经网络识别对象的空间配置
SpatialSim: Recognizing Spatial Configurations of Objects with Graph Neural Networks
论文作者
论文摘要
识别对象组的精确几何形态是人类空间认知的关键能力，但迄今为止深度学习文献中很少研究。特别是，一个基本问题是机器如何学习和比较几何空间配置的类别，而几何空间配置与外部观察者的观点不变。在本文中，我们做出了两个关键贡献。首先，我们提出了一种新型的几何推理基准（空间相似性），并认为该基准的进步将为解决现实世界中的这一挑战铺平道路。该基准由两个任务组成：识别和比较，每个任务都以越来越高的难度级别实例化。其次，我们研究了完全连接的消息传递图形神经网络（MPGNN）表现出的关系电感偏见对于解决这些任务是有用的，并显示了它们在较少的关系基线（如深度设置）和非结构化模型（如多层型号的意识）上的优势。最后，我们重点介绍了这些任务中GNN的当前限制。
Recognizing precise geometrical configurations of groups of objects is a key capability of human spatial cognition, yet little studied in the deep learning literature so far. In particular, a fundamental problem is how a machine can learn and compare classes of geometric spatial configurations that are invariant to the point of view of an external observer. In this paper we make two key contributions. First, we propose SpatialSim (Spatial Similarity), a novel geometrical reasoning benchmark, and argue that progress on this benchmark would pave the way towards a general solution to address this challenge in the real world. This benchmark is composed of two tasks: Identification and Comparison, each one instantiated in increasing levels of difficulty. Secondly, we study how relational inductive biases exhibited by fully-connected message-passing Graph Neural Networks (MPGNNs) are useful to solve those tasks, and show their advantages over less relational baselines such as Deep Sets and unstructured models such as Multi-Layer Perceptrons. Finally, we highlight the current limits of GNNs in these tasks.
