关键词：机器学习系统；贝叶斯模型；视觉刺激
摘 要：We present a system for learning nouns directly from images, using probabilistic predictions generated by visual classifiers as the input to Bayesian word learning, and compare this system to human performance in an automated, large-scale experiment. The system captures a significant proportion of the variance in human responses. Combining the uncertain outputs of the visual classifiers with the ability to identify an appropriate level of abstraction that comes from Bayesian word learning allows the system to outperform alternatives that either cannot deal with visual stimuli or use a more conventional computer vision approach.