# Continual Visual and Verbal Learning Through a Child's Egocentric Input

arXiv: https://arxiv.org/abs/2606.05115 · HTML: https://mlsanity.com/paper/2606.05115/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 26 references that are in the map.
- First posted on arXiv 3 Jun 2026.

## Builds on (most cited first, 15 of 15)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks](https://mlsanity.com/paper/1710.11063/) (2017)
- [Self-Supervised Models are Continual Learners](https://mlsanity.com/paper/2112.04215/) (2021)
- [Continual Unsupervised Representation Learning](https://mlsanity.com/paper/1910.14481/) (2019)
- [Representational Continuity for Unsupervised Continual Learning](https://mlsanity.com/paper/2110.06976/) (2021)
- [Self-supervised learning through the eyes of a child](https://mlsanity.com/paper/2007.16189/) (2020)
- [Continually Learning Self-Supervised Representations with Projected Functional Regularization](https://mlsanity.com/paper/2112.15022/) (2021)
- [The Challenges of Continuous Self-Supervised Learning](https://mlsanity.com/paper/2203.12710/) (2022)
- [Continual Multimodal Contrastive Learning](https://mlsanity.com/paper/2503.14963/) (2025)
- [Integrating Present and Past in Unsupervised Continual Learning](https://mlsanity.com/paper/2404.19132/) (2024)
- [Memory Storyboard: Leveraging Temporal Segmentation for Streaming Self-Supervised Learning from Egocentric Videos](https://mlsanity.com/paper/2501.12254/) (2025)
- [BabyVLM: Data-Efficient Pretraining of VLMs Inspired by Infant Learning](https://mlsanity.com/paper/2504.09426/) (2025)
- [On the robustness of modeling grounded word learning through a child's egocentric input](https://mlsanity.com/paper/2507.14749/) (2025)
- [BabyVLM-V2: Toward Developmentally Grounded Pretraining and Benchmarking of Vision Foundation Models](https://mlsanity.com/paper/2512.10932/) (2025)

## Abstract (arXiv)

Children learn the meanings of words from a continuous, temporally structured stream of egocentric experience. Recent work shows that neural networks can also learn word-referent mappings from a child's egocentric video recordings, but they cycle through the shuffled data for hundreds of epochs, contrasting with how children actually encounter their environment. We introduce BabyCL, a continual multimodal learning framework that processes the SAYCam dataset in a single chronological pass, combining streaming visual representation learning with an image-text contrastive objective. BabyCL combines a multi-stage temporal segmentation of the stream with a dual replay buffer that independently manages visual and multimodal histories, and it is jointly trained with three contrastive losses on a shared backbone. Under a matched optimization budget, BabyCL outperforms streaming learning baselines on the SAYCam Labeled-S 4AFC benchmark, substantially narrowing the gap to an upper bound of offline training. Ablations show that the gains are robust to the length of the online temporal segmentation window and the eviction rule of the replay buffer. Together, these results show that meaningful word-referent mappings can emerge under training conditions much closer to a child's actual experience.