Learning Concept Embeddings with Combined Human-Machine Expertise

ICCV 2015 Michael J. WilberIljung S. KwakDavid KriegmanSerge Belongie

This paper presents our work on "SNaCK," a low-dimensional concept embedding algorithm that combines human expertise with automatic machine similarity kernels. Both parts are complimentary: human insight can capture relationships that are not apparent from the object's visual similarity and the machine can help relieve the human from having to exhaustively specify many constraints... (read more)

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