  Abstract Visual Reasoning (AVR) comprises a wide selection of various
problems similar to those used in human IQ tests. Recent years have brought
dynamic progress in solving particular AVR tasks, however, in the contemporary
literature AVR problems are largely dealt with in isolation, leading to highly
specialized task-specific methods. With the aim of developing universal
learning systems in the AVR domain, we propose the unified model for solving
Single-Choice Abstract visual Reasoning tasks (SCAR), capable of solving
various single-choice AVR tasks, without making any a priori assumptions about
the task structure, in particular the number and location of panels. The
proposed model relies on a novel Structure-Aware dynamic Layer (SAL), which
adapts its weights to the structure of the considered AVR problem. Experiments
conducted on Raven's Progressive Matrices, Visual Analogy Problems, and Odd One
Out problems show that SCAR (SAL-based models, in general) effectively solves
diverse AVR tasks, and its performance is on par with the state-of-the-art
task-specific baselines. What is more, SCAR demonstrates effective knowledge
reuse in multi-task and transfer learning settings. To our knowledge, this work
is the first successful attempt to construct a general single-choice AVR solver
relying on self-configurable architecture and unified solving method. With this
work we aim to stimulate and foster progress on task-independent research paths
in the AVR domain, with the long-term goal of development of a general AVR
solver.
