论文标题
分层关系推断
Hierarchical Relational Inference
论文作者
论文摘要
现实世界中常识性的物理推理需要学习对象的相互作用及其动态。但是，抽象对象的概念涵盖了各种各样的物理对象，这些物理对象在其支持的复杂行为方面有很大差异。为了解决这个问题，我们提出了一种新颖的物理推理方法，该方法将对象成为可能在局部行为的部分的层次结构，但在全球范围内作为一个整体的行为。与先前的方法不同，我们的方法直接从原始的视觉图像中以无监督的方式学习，以发现对象，部分及其关系。它明确区分了多个级别的抽象，并在建模合成和现实世界视频时的基线方面有所改善。
Common-sense physical reasoning in the real world requires learning about the interactions of objects and their dynamics. The notion of an abstract object, however, encompasses a wide variety of physical objects that differ greatly in terms of the complex behaviors they support. To address this, we propose a novel approach to physical reasoning that models objects as hierarchies of parts that may locally behave separately, but also act more globally as a single whole. Unlike prior approaches, our method learns in an unsupervised fashion directly from raw visual images to discover objects, parts, and their relations. It explicitly distinguishes multiple levels of abstraction and improves over a strong baseline at modeling synthetic and real-world videos.
