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.

翻译：抽象视觉推理（AVR）包含一系列类似于人类智商测试中使用的多样化问题。近年来，在解决特定AVR任务方面取得了显著进展，然而，当前文献中大多孤立地处理AVR问题，导致高度专业化的任务特定方法出现。为了在AVR领域开发通用学习系统，我们提出了一种用于解决单选择抽象视觉推理任务（SCAR）的统一模型，该模型能够解决多种单选择AVR任务，而无需对任务结构（特别是面板的数量和位置）做出任何先验假设。提出的模型依赖于一种新颖的结构感知动态层（SAL），该层能够根据所考虑的AVR问题的结构调整其权重。在瑞文渐进矩阵、视觉类比问题和异常检测问题上进行的实验表明，SCAR（基于SAL的模型，总体上）能有效解决多样化的AVR任务，其性能与最先进的任务特定基线相当。此外，SCAR在多任务和迁移学习设置中展示了有效的知识重用。据我们所知，这项工作首次成功构建了一个依赖自配置架构和统一求解方法的通用单选择AVR求解器。通过这项工作，我们旨在激发和促进AVR领域内任务无关的研究路径，长期目标是开发一个通用的AVR求解器。