The BabyView dataset: High-resolution egocentric videos of infants' and young children's everyday experiences

TL;DR

The BabyView dataset provides high-resolution, egocentric videos of infants aged 6 months to 3 years, enabling research on human development and AI models through diverse, annotated, real-world data.

Contribution

This is the first large-scale, high-resolution egocentric infant video dataset with extensive annotations, facilitating development and evaluation of models in developmental and computer vision tasks.

Findings

Models trained on BabyView perform worse than on curated datasets.

Performance improves with dataset size but remains below human levels.

The dataset presents a challenge for developing human-like AI systems.

Abstract

Human children far exceed modern machine learning algorithms in their sample efficiency, achieving high performance in key domains with much less data than current models. This ''data gap'' is a key challenge both for building intelligent artificial systems and for understanding human development. Egocentric video capturing children's experience--their ''training data''--is a key ingredient for comparison of humans and models and for the development of algorithmic innovations to bridge this gap. Yet there are few such datasets available, and extant data are low-resolution, have limited metadata, and importantly, represent only a small set of children's experiences. Here, we provide the first release of a large developmental egocentric video dataset--the BabyView dataset--recorded using a high-resolution camera with a large vertical field-of-view and gyroscope/accelerometer data. This 868 hour dataset includes egocentric videos from children spanning 6 months to 3 years of age in longitudinal, at-home contexts. We provide gold-standard annotations for the evaluation of speech transcription, speaker diarization, and human pose estimation, and evaluate models in each of these domains. We train self-supervised language and vision models and evaluate their transfer to out-of-distribution tasks, including syntactic structure learning, object recognition, depth estimation, and image segmentation. Although performance in each domain scales with dataset size, overall performance is relatively lower than when models are trained on curated datasets, especially in the visual domain. Our dataset stands as an open challenge for robust, human-like AI systems: how can such systems achieve human-levels of success on the same scale and distribution of training data as humans?