27 citations · 66 across the 12 of their papers we have counts for
9 papers · 1 filter
Continual Visual and Verbal Learning Through a Child's Egocentric Input
Xiaoyang Jiang, Yanlai Yang, Kenneth A. Norman +2
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 m…
Beyond the Doors of Perception: Vision Transformers Represent Relations Between Objects
Michael A. Lepori, Alexa R. Tartaglini, Wai Keen Vong +3
Though vision transformers (ViTs) have achieved state-of-the-art performance in a variety of settings, they exhibit surprising failures when performing tasks involving visual relat…
Self-supervised learning of video representations from a child's perspective
A. Emin Orhan, Wentao Wang, Alex N. Wang +2
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 experie…
Deep Neural Networks Can Learn Generalizable Same-Different Visual Relations
Alexa R. Tartaglini, Sheridan Feucht, Michael A. Lepori +4
Although deep neural networks can achieve human-level performance on many object recognition benchmarks, prior work suggests that these same models fail to learn simple abstract re…
A Developmentally-Inspired Examination of Shape versus Texture Bias in Machines
Alexa R. Tartaglini, Wai Keen Vong, Brenden M. Lake
Early in development, children learn to extend novel category labels to objects with the same shape, a phenomenon known as the shape bias. Inspired by these findings, Geirhos et al…
Flexible Compositional Learning of Structured Visual Concepts
Yanli Zhou, Brenden M. Lake
Humans are highly efficient learners, with the ability to grasp the meaning of a new concept from just a few examples. Unlike popular computer vision systems, humans can flexibly l…