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Abstract Visual Reasoning (AVR) problems are commonly used to approximatehuman intelligence. They test the ability of applying previously gainedknowledge, experience and skills in a completely new setting, which makes themparticularly well-suited for this task. Recently, the AVR problems have becomepopular as a proxy to study machine intelligence, which has led to emergence ofnew distinct types of problems and multiple benchmark sets. In this work wereview this emerging AVR research and propose a taxonomy to categorise the AVRtasks along 5 dimensions: input shapes, hidden rules, target task, cognitivefunction, and main challenge. The perspective taken in this survey allows tocharacterise AVR problems with respect to their shared and distinct properties,provides a unified view on the existing approaches for solving AVR tasks, showshow the AVR problems relate to practical applications, and outlines promisingdirections for future work. One of them refers to the observation that in themachine learning literature different tasks are considered in isolation, whichis in the stark contrast with the way the AVR tasks are used to measure humanintelligence, where multiple types of problems are combined within a single IQtest.

A Review of Emerging Research Directions in Abstract Visual Reasoning