2009
NIPS
NeurIPS 2009
Individuation, Identification and Object Discovery
Abstract
Humans are typically able to infer how many objects their environment contains and to recognize when the same object is encountered twice. We present a simple statistical model that helps to explain these abilities and evaluate it in three behavioral experiments. Our first experiment suggests that humans rely on prior knowledge when deciding whether an object token has been previously encountered. Our second and third experiments suggest that humans can infer how many objects they have seen and can learn about categories and their properties even when they are uncertain about which tokens are instances of the same object.
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Interdisciplinary Bridge
— Computer Vision and Interdisciplinary and Machine Learning
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Keyword Pioneer
— token identification
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Cross-Pollinator
— Artificial Intelligence, Computer Vision, Deep Learning, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Speech & Audio
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Trend Setter
— Computer Vision
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Hot Topic Early Bird
— probabilistic modeling
Authors
Topics
Artificial Intelligence > Bayesian & Probabilistic > Probabilistic Modeling
Machine Learning > Core Methods > Clustering
Computer Vision > Analysis > Object Detection
Computer Vision > Analysis > Scene Understanding
Interdisciplinary > Cognitive Science > Cognitive Modeling
Interdisciplinary > Cognitive Science > Perception
Artificial Intelligence > Core AI > Computer Vision