2020/07/09 by Xiangru Tang, Haoyuan Wang, Tang, Xiangru +5 · 2 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · Psychology · #Artificial Intelligence (cs.AI) #Artificial intelligence #Cognition #Computer Vision and Pattern Recognition (cs.CV) #Computer science #Deductive reasoning #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Focus (optics) #Generalization #Granularity #Human–computer interaction #Machine Learning in Bioinformatics #Neural Networks and Applications #Programming language #Psychology #Raven's Progressive Matrices #Visual reasoning #cs.AI #cs.CV
paper · pdf · doi:10.48550/arxiv.2007.04670
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2020/07/09 · arxiv created 2020/07/10 · arxiv updated 2020/07/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Abstract visual reasoning connects mental abilities to the physical world, which is a crucial factor in cognitive development. Most toddlers display sensitivity to this skill, but it is not easy for machines. Aimed at it, we focus on the Raven Progressive Matrices Test, designed to measure cognitive reasoning. Recent work designed some black-boxes to solve it in an end-to-end fashion, but they are incredibly complicated and difficult to explain. Inspired by cognitive studies, we propose a Multi-Granularity Modularized Network (MMoN) to bridge the gap between the processing of raw sensory information and symbolic reasoning. Specifically, it learns modularized reasoning functions to model the semantic rule from the visual grounding in a neuro-symbolic and semi-supervision way. To comprehensively evaluate MMoN, our experiments are conducted on the dataset of both seen and unseen reasoning rules. The result shows that MMoN is well suited for abstract visual reasoning and also explainable on the generalization test.