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Toddler-Inspired Visual Object Learning
Sven Bambach · David Crandall · Linda Smith · Chen Yu

Thu Dec 06 07:45 AM -- 09:45 AM (PST) @ Room 210 #96

Real-world learning systems have practical limitations on the quality and quantity of the training datasets that they can collect and consider. How should a system go about choosing a subset of the possible training examples that still allows for learning accurate, generalizable models? To help address this question, we draw inspiration from a highly efficient practical learning system: the human child. Using head-mounted cameras, eye gaze trackers, and a model of foveated vision, we collected first-person (egocentric) images that represents a highly accurate approximation of the "training data" that toddlers' visual systems collect in everyday, naturalistic learning contexts. We used state-of-the-art computer vision learning models (convolutional neural networks) to help characterize the structure of these data, and found that child data produce significantly better object models than egocentric data experienced by adults in exactly the same environment. By using the CNNs as a modeling tool to investigate the properties of the child data that may enable this rapid learning, we found that child data exhibit a unique combination of quality and diversity, with not only many similar large, high-quality object views but also a greater number and diversity of rare views. This novel methodology of analyzing the visual "training data" used by children may not only reveal insights to improve machine learning, but also may suggest new experimental tools to better understand infant learning in developmental psychology.

Author Information

Sven Bambach (The Research Institute at Nationwide Children's Hospital)
David Crandall (Indiana University)
Linda Smith (Indiana University)

Linda B. Smith, Distinguished Professor at Indiana University Bloomington, is an internationally recognized leader in cognitive science and cognitive development. Taking a complex systems perspective, she seeks to understand the interdependencies among perceptual, motor and cognitive developments during the first three years of post-natal life. Using wearable sensors, including head-mounted cameras, she studies how the young learner’s own behavior creates the statistical structure of the learning environments with a current focus on developmentally changing visual statistics at the scale of everyday life and their role in motor, perceptual, and language development. The work has led to novel insights about the statistics of self-generated experiences and their role in rapid learning and innovative generalization from sparse and limited experience and challenges current massive-data approaches in AI. The work also motivates her current efforts on defining and promoting a precision (or individualized) developmental science, one that determines the multiple causes and interacting factors that create children’s individual developmental pathways. Smith received her PhD from the University of Pennsylvania in 1977 and immediately joined the faculty at Indiana University. Her work has been continuously funded by the National Science Foundation and/or the National Institutes of Health since 1978. She won the David E. Rumelhart Prize for Theoretical Contributions to Cognitive Science, the American Psychological Association Award for Distinguished Scientific Contributions, the William James Fellow Award from the American Psychological Society, the Norman Anderson Lifetime Achievement Award, and the Koffka Medal. She is an elected member of both the National Academy of Sciences and the American Academy of Arts and Science.

Chen Yu (Indiana University)

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