BabyCL

BabyCL: Continual multimodal learning from egocentric child video in a single chronological pass

Researchers introduce BabyCL, a continual learning framework that processes the SAYCam egocentric child video dataset in a single chronological pass rather than shuffled multi-epoch training, more closely mimicking how children actually encounter their environment. The system combines streaming visual representation learning with image-text contrastive objectives, a multi-stage temporal segmentation, and a dual replay buffer managing visual and multimodal histories. BabyCL outperforms streaming baselines on the SAYCam Labeled-S 4AFC benchmark under matched compute budgets, substantially closing the gap to offline training upper bounds. The work advances understanding of whether neural networks can acquire word-referent mappings under biologically plausible training conditions.