Constellation: Learning relational abstractions over objects for compositional imagination

• URL: http://arxiv.org/abs/2107.11153v1
• Date: Fri, 23 Jul 2021 11:59:40 GMT
• Title: Constellation: Learning relational abstractions over objects for compositional imagination
• Authors: James C.R. Whittington, Rishabh Kabra, Loic Matthey, Christopher P. Burgess, Alexander Lerchner
• Abstract summary: We introduce Constellation, a network that learns relational abstractions of static visual scenes. This work is a first step in the explicit representation of visual relationships and using them for complex cognitive procedures.
• Score: 64.99658940906917
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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