Humans continue to outperform modern AI systems in their ability to flexibly
parse and understand complex visual scenes. Here, we present a novel module for
visual reasoning, the Guided Attention Model for (visual) Reasoning (GAMR),
which instantiates an active vision theory -- positing that the brain solves
complex visual reasoning problems dynamically -- via sequences of attention
shifts to select and route task-relevant visual information into memory.
Experiments on an array of visual reasoning tasks and datasets demonstrate
GAMR's ability to learn visual routines in a robust and sample-efficient
manner. In addition, GAMR is shown to be capable of zero-shot generalization on
completely novel reasoning tasks. Overall, our work provides computational
support for cognitive theories that postulate the need for a critical interplay
between attention and memory to dynamically maintain and manipulate
task-relevant visual information to solve complex visual reasoning tasks.

本文介绍了一个用于视觉推理的新模块 —— 带有引导注意力的视觉推理模型（GAMR），它通过选择和路由任务相关的视觉信息进入记忆的注意力移动序列来实现主动视觉理论。实验证明，GAMR 能够以稳健和高效的方式学习视觉任务，并能够在全新的推理任务上实现零 - shot 泛化。研究结果支持需要在注意力和记忆之间进行关键相互作用，以动态维护和操纵任务相关视觉信息来解决复杂视觉推理任务的认知理论。