  The field of Abstract Visual Reasoning (AVR) encompasses a wide range of
problems, many of which are inspired by human IQ tests. The variety of AVR
tasks has resulted in state-of-the-art AVR methods being task-specific
approaches. Furthermore, contemporary methods consider each AVR problem
instance not as a whole, but in the form of a set of individual panels with
particular locations and roles (context vs. answer panels) pre-assigned
according to the task-specific arrangements. While these highly specialized
approaches have recently led to significant progress in solving particular AVR
tasks, considering each task in isolation hinders the development of universal
learning systems in this domain. In this paper, we introduce a unified view of
AVR tasks, where each problem instance is rendered as a single image, with no a
priori assumptions about the number of panels, their location, or role. The
main advantage of the proposed unified view is the ability to develop universal
learning models applicable to various AVR tasks. What is more, the proposed
approach inherently facilitates transfer learning in the AVR domain, as various
types of problems share a common representation. The experiments conducted on
four AVR datasets with Raven's Progressive Matrices and Visual Analogy
Problems, and one real-world visual analogy dataset show that the proposed
unified representation of AVR tasks poses a challenge to state-of-the-art Deep
Learning (DL) AVR models and, more broadly, contemporary DL image recognition
methods. In order to address this challenge, we introduce the Unified Model for
Abstract Visual Reasoning (UMAVR) capable of dealing with various types of AVR
problems in a unified manner. UMAVR outperforms existing AVR methods in
selected single-task learning experiments, and demonstrates effective knowledge
reuse in transfer learning and curriculum learning setups.
