Recent work in cognitive reasoning and computer vision has engendered an increasing popularity for the Violation-of-Expectation (VoE) paradigm in synthetic datasets. Inspired by work in infant psychology, researchers have started evaluating a model's ability to discriminate between expected and surprising scenes as a sign of its reasoning ability. Existing VoE-based 3D datasets in physical reasoning only provide vision data. However, current cognitive models of physical reasoning by psychologists reveal infants create high-level abstract representations of objects and interactions. Capitalizing on this knowledge, we propose AVoE: a synthetic 3D VoE-based dataset that presents stimuli from multiple novel sub-categories for five event categories of physical reasoning. Compared to existing work, AVoE is armed with ground-truth labels of abstract features and rules augmented to vision data, paving the way for high-level symbolic predictions in physical reasoning tasks.
翻译:最近在认知推理和计算机视觉方面开展的工作使合成数据集中违反预期(VoE)范式越来越受欢迎。在婴儿心理学工作的启发下,研究人员开始评估模型对预期和令人惊讶的场景进行区分的能力,作为其推理能力的标志。在物理推理中现有的基于VoE的三维数据集仅提供了视觉数据。然而,心理学家目前对物理推理的认知模型揭示婴儿对物体和相互作用产生了高度抽象的描述。利用这一知识,我们提议AVE:合成3DVoE:基于合成的3DVoE数据集,该数据集从多种新颖的子类中为五类物理推理提供刺激。与现有工作相比,AVoE武装了抽象特征和规则的地面图象标签,加强了视觉数据,为在物理推理任务中进行高层次的象征性预测铺平了道路。