Deep Learning Methods for Abstract Visual Reasoning: A Survey on Raven's
Progressive Matrices
- URL: http://arxiv.org/abs/2201.12382v1
- Date: Fri, 28 Jan 2022 19:24:30 GMT
- Title: Deep Learning Methods for Abstract Visual Reasoning: A Survey on Raven's
Progressive Matrices
- Authors: Miko{\l}aj Ma{\l}ki\'nski and Jacek Ma\'ndziuk
- Abstract summary: We focus on the most common type of tasks -- the Raven's Progressive Matrices ( RPMs) -- and provide a review of the learning methods and deep neural models applied to solve RPMs.
We conclude the paper by demonstrating how real-world problems can benefit from the discoveries of RPM studies.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Abstract visual reasoning (AVR) domain encompasses problems solving which
requires the ability to reason about relations among entities present in a
given scene. While humans, generally, solve AVR tasks in a ``natural'' way,
even without prior experience, this type of problems has proven difficult for
current machine learning systems. The paper summarises recent progress in
applying deep learning methods to solving AVR problems, as a proxy for studying
machine intelligence. We focus on the most common type of AVR tasks -- the
Raven's Progressive Matrices (RPMs) -- and provide a comprehensive review of
the learning methods and deep neural models applied to solve RPMs, as well as,
the RPM benchmark sets. Performance analysis of the state-of-the-art approaches
to solving RPMs leads to formulation of certain insights and remarks on the
current and future trends in this area. We conclude the paper by demonstrating
how real-world problems can benefit from the discoveries of RPM studies.
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