Real-world visual statistics and infants' first-learned object names

By Elizabeth M. Clerkin, Elizabeth Hart, James M. Rehg, Chen Yu, Linda B. Smith. 2017

213 citations recorded by the source corpus.

Subfields: Automotive Engineering, Developmental and Educational Psychology, Experimental and Cognitive Psychology, Representation & Self-Supervised Learning

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Citations in the corpus

Environmental drivers of systematicity and generalization in a situated agent (2019, background): “…in artiﬁcial neural networks if those networks are afforded access to a rich, interactive, multimodal stream of stimuli that better matches the experience of an embodied human learner (Clerkin et al., 2017; Kellman & Arterberry, 2000; James et al., 2014; Yurovsky et al., 2013; Anderson, 2003).”
Emergent Systematic Generalization in a Situated Agent (2019, background): “…in artiﬁcial neural networks if those networks are afforded access to a rich, interactive, multimodal stream of stimuli that better matches the experience of an embodied human learner (Clerkin et al., 2017; Kellman & Arterberry, 2000; James et al., 2014; Yurovsky et al., 2013; Anderson, 2003).”
A Developmental Approach to Machine Learning? (2017, background): “Likewise, the objects in infants’ visual environments are also extremely right skewed, with some object categories much more frequent that others (Clerkin et al., 2017).”
Incremental Object Learning From Contiguous Views (2019, background): “Research in child development [8, 49, 22] has identified five key properties of infants’ play experiences.”
SAYCam: A Large, Longitudinal Audiovisual Dataset Recorded From the Infant’s Perspective (2020, background): “These datasets also let researchers access some of the “source data” for children’s generalizations about object identity or early word learning (Clerkin et al., 2017).”
Continual egocentric object recognition (2019, background): “This latter setting is highly beneﬁcial in terms of user effort, as the amount of supervision that F OLLOWER requests to match the performances of FULL OWER is far less than the one it needs in the random case, in agreement with what believed about infant learning [4].”