Marieke Mur. Bridging visual developmental neuroscience and deep learning: challenges and future directions. Journal of Vision, 23(9):4680-4680, 2023.
Bruno A Olshausen and David J Field. Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature, 381(6583):607-609, 1996.
A Emin Orhan and Brenden M Lake. What can generic neural networks learn from a child's visual experience? arXiv preprint arXiv:2305.15372, 2023.
Emin Orhan, Vaibhav Gupta, and Brenden M Lake. Self-supervised learning through the eyes of a child. Advances in Neural Information Processing Systems, 33:9960-9971, 2020.
Eleanor Rosch and Carolyn B Mervis. Family resemblances: Studies in the internal structure of categories. Cognitive psychology, 7(4):573-605, 1975.
Luca Saglietti, Stefano Mannelli, and Andrew Saxe. An analytical theory of curriculum learning in teacher-student networks. Advances in Neural Information Processing Systems, 35:21113-21127, 2022.
Madeline C Schiappa, Yogesh S Rawat, and Mubarak Shah. Self-supervised learning for videos: A survey. ACM Computing Surveys, 2022.
Martin Schrimpf, Idan Asher Blank, Greta Tuckute, Carina Kauf, Eghbal A Hosseini, Nancy Kanwisher, Joshua B Tenenbaum, and Evelina Fedorenko. The neural architecture of language: Integrative modeling converges on predictive processing. Proceedings of the National Academy of Sciences, 118(45):e2105646118, 2021.
Saber Sheybani, Zoran Tiganj, Justin N Wood, and Linda B Smith. Slow change: An analysis of infant egocentric visual experience. Journal of Vision, 23(9):4685-4685, 2023.
Ravid Shwartz-Ziv and Naftali Tishby. Opening the black box of deep neural networks via information. arXiv preprint arXiv:1703.00810, 2017.
Linda B Smith and Lauren K Slone. A developmental approach to machine learning? Frontiers in psychology, page 2124, 2017.
Linda B Smith, Swapnaa Jayaraman, Elizabeth Clerkin, and Chen Yu. The developing infant creates a curriculum for statistical learning. Trends in cognitive sciences, 22(4):325-336, 2018.
Khurram Soomro, Amir Roshan Zamir, and Mubarak Shah. Ucf101: A dataset of 101 human actions classes from videos in the wild. arXiv preprint arXiv:1212.0402, 2012.
Elizabeth S Spelke. Preferential-looking methods as tools for the study of cognition in infancy. Ablex Publishing, 1985.
Craig A Stewart, Von Welch, Beth Plale, Geoffrey Fox, Marlon Pierce, and Thomas Sterling. Indiana university pervasive technology institute. 2017.
Jessica Sullivan, Michelle Mei, Andrew Perfors, Erica Wojcik, and Michael C Frank. Saycam: A large, longitudinal audiovisual dataset recorded from the infant's perspective. Open mind, 5:20-29, 2021.
Zhan Tong, Yibing Song, Jue Wang, and Limin Wang. Videomae: Masked autoencoders are data-efficient learners for self-supervised video pre-training. arXiv preprint arXiv:2203.12602, 2022.
Du Tran, Lubomir Bourdev, Rob Fergus, Lorenzo Torresani, and Manohar Paluri. Learning spatiotemporal features with 3d convolutional networks. In Proceedings of the IEEE international conference on computer vision, pages 4489-4497, 2015.
Xiaohan Wang, Tengyu Ma, James Ainooson, Seunghwan Cha, Xiaotian Wang, Azhar Molla, and Maithilee Kunda. The toybox dataset of egocentric visual object transformations. arXiv preprint arXiv:1806.06034, 2018.