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Constellation: Learning relational abstractions over objects for compositional imagination

2021-07-23 11:59:40
James C.R. Whittington, Rishabh Kabra, Loic Matthey, Christopher P. Burgess, Alexander Lerchner
arXiv_AI
       
Abstract

Learning structured representations of visual scenes is currently a major bottleneck to bridging perception with reasoning. While there has been exciting progress with slot-based models, which learn to segment scenes into sets of objects, learning configurational properties of entire groups of objects is still under-explored. To address this problem, we introduce Constellation, a network that learns relational abstractions of static visual scenes, and generalises these abstractions over sensory particularities, thus offering a potential basis for abstract relational reasoning. We further show that this basis, along with language association, provides a means to imagine sensory content in new ways. This work is a first step in the explicit representation of visual relationships and using them for complex cognitive procedures.

Abstract (translated)

URL

https://arxiv.org/abs/2107.11153

PDF

https://arxiv.org/pdf/2107.11153.pdf

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