Hierarchical Relational Inference

Authors

  • Aleksandar Stanić IDSIA
  • Sjoerd van Steenkiste IDSIA
  • Jürgen Schmidhuber IDSIA

Keywords:

Representation Learning, Neuro-Symbolic AI (NSAI)

Abstract

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.

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Published

2021-05-18

How to Cite

Stanić, A., van Steenkiste, S., & Schmidhuber, J. (2021). Hierarchical Relational Inference. Proceedings of the AAAI Conference on Artificial Intelligence, 35(11), 9730-9738. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17170

Issue

Section

AAAI Technical Track on Machine Learning IV