Abstract Visual Reasoning

Abstract visual reasoning (AVR) focuses on developing artificial intelligence systems capable of understanding and reasoning about abstract patterns and relationships in visual data, mirroring human cognitive abilities tested in IQ assessments like Raven's Progressive Matrices. Current research emphasizes creating unified models that can handle diverse AVR tasks, rather than task-specific approaches, often employing deep learning architectures such as transformers and object-centric models incorporating attention mechanisms and structured representations (e.g., schemas). Progress in AVR holds significant implications for advancing artificial general intelligence and has potential applications in various fields requiring complex visual interpretation and reasoning.