Common fate based object learning in machines and humans

Abstract

An essential feature of human visual perception is the segmentation of scenes into separable object representations. A rich body of work shows that the Gestalt principle of common fate plays an important role in this capability, both for the development of object perception in infants and as a grouping cue in the fully developed human visual system. Unlike humans, modern object learning methods in computer vision commonly rely on large-scale supervised training. Recently however, machine learning models have been proposed that learn to segment and individually represent objects in a scene without supervision. Here, we show that leveraging the Gestalt principle of common fate can improve these unsupervised object learning models. We build on an unsupervised motion segmentation algorithm that implements the principle of common fate by clustering pixels that exhibit similar motion. Those candidate …