Proceedings of the Annual Meeting of the Cognitive Science Society

Early visual object recognition in a world full of cluttered vi-sual information is a complicated task at which toddlers areincredibly efficient. In their everyday lives, toddlers con-stantly create learning experiences by actively manipulatingobjects and thus self-selecting object views for visual learn-ing. The work in this paper is based on the hypothesis that ac-tive viewing and exploration of toddlers actually creates high-quality training data for object recognition. We tested thisidea by collecting egocentric video data of free toy play be-tween toddler-parent dyads, and used it to train state-of-the-artmachine learning models (Convolutional Neural Networks, orCNNs). Our results show that the data collected by parentsand toddlers have different visual properties and that CNNscan take advantage of these differences to learn toddler-basedobject models that outperform their parent counterparts in aseries of controlled simulations.