Children learn powerful internal models of the world around them from a few years of egocentric visual experience. Can such internal models be learned from a child's visual experience with highly generic learning algorithms or do they require strong inductive biases? Recent advances in collecting large-scale, longitudinal, developmentally realistic video datasets and generic self-supervised learning (SSL) algorithms are allowing us to begin to tackle this nature vs. nurture question. However, existing work typically focuses on image-based SSL algorithms and visual capabilities that can be learned from static images (e.g. object recognition), thus ignoring temporal aspects of the world. To close this gap, here we train self-supervised video models on longitudinal, egocentric headcam recordings collected from a child over a two year period in their early development (6-31 months). The resulting models are highly effective at facilitating the learning of action concepts from a small number of labeled examples; they have favorable data size scaling properties; and they display emergent video interpolation capabilities. Video models also learn more robust object representations than image-based models trained with the exact same data. These results suggest that important temporal aspects of a child's internal model of the world may be learnable from their visual experience using highly generic learning algorithms and without strong inductive biases.

翻译：儿童通过数年自我中心的视觉经验，习得了关于周围世界的强大内在模型。这种内在模型能否通过高度通用的学习算法从儿童视觉经验中习得？抑或需要强归纳偏置？近年来，大规模、纵向且符合发展现实的视频数据集以及通用自监督学习算法的进展，使我们开始能够探讨这一先天与后天的问题。然而现有工作通常聚焦于基于图像的自监督算法及可从静态图像中习得的视觉能力（例如物体识别），忽略了世界的时序特性。为填补这一空白，本研究在收集自一名儿童早期发育阶段（6-31个月）为期两年的纵向自我中心头戴摄像机记录上，训练了自监督视频模型。所得模型在少量标注样本下即可高效促进动作概念的学习；展现出良好的数据规模缩放特性；并呈现出突现的视频插值能力。与使用完全相同数据训练的基于图像的模型相比，视频模型还能习得更鲁棒的物体表征。这些结果表明，儿童内在世界模型的重要时序特性，可能通过高度通用的学习算法从其视觉经验中习得，且无需强归纳偏置。