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Perceptual Annotation: Measuring Human Vision to Improve Computer Vision
2014
IEEE Transactions on Pattern Analysis and Machine Intelligence
For many problems in computer vision, human learners are considerably better than machines. Humans possess highly accurate internal recognition and learning mechanisms that are not yet understood, and they frequently have access to more extensive training data through a lifetime of unbiased experience with the visual world. We propose to use visual psychophysics to directly leverage the abilities of human subjects to build better machine learning systems. First, we use an advanced online
doi:10.1109/tpami.2013.2297711
pmid:26353347
fatcat:25xqb43n5rbjbitoxbjhh2xada