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Found 7 papers, 1 papers with code
A Cognitively-Inspired Neural Architecture for Visual Abstract Reasoning Using Contrastive Perceptual and Conceptual Processing
We introduce a new neural architecture for solving visual abstract reasoning tasks inspired by human cognition, specifically by observations that human abstract reasoning often interleaves perceptual and conceptual processing as part of a flexible, iterative, and dynamic cognitive process.
A Computational Account Of Self-Supervised Visual Learning From Egocentric Object Play
Research in child development has shown that embodied experience handling physical objects contributes to many cognitive abilities, including visual learning.
A Neurodiversity-Inspired Solver for the Abstraction \& Reasoning Corpus (ARC) Using Visual Imagery and Program Synthesis
Core knowledge about physical objects -- e. g., their permanency, spatial transformations, and interactions -- is one of the most fundamental building blocks of biological intelligence across humans and non-human animals.
Automatic Item Generation of Figural Analogy Problems: A Review and Outlook
Figural analogy problems have long been a widely used format in human intelligence tests.
Variable-Viewpoint Representations for 3D Object Recognition
For the problem of 3D object recognition, researchers using deep learning methods have developed several very different input representations, including "multi-view" snapshots taken from discrete viewpoints around an object, as well as "spherical" representations consisting of a dense map of essentially ray-traced samples of the object from all directions.
Quantifying Human Behavior on the Block Design Test Through Automated Multi-Level Analysis of Overhead Video
The block design test is a standardized, widely used neuropsychological assessment of visuospatial reasoning that involves a person recreating a series of given designs out of a set of colored blocks.
The Toybox Dataset of Egocentric Visual Object Transformations
In object recognition research, many commonly used datasets (e. g., ImageNet and similar) contain relatively sparse distributions of object instances and views, e. g., one might see a thousand different pictures of a thousand different giraffes, mostly taken from a few conventionally photographed angles.
