  Object classes that surround us have a natural tendency to emerge at varying
levels of abstraction. We propose a Bayesian approach to zero-shot learning
(ZSL) that introduces the notion of meta-classes and implements a Bayesian
hierarchy around these classes to effectively blend data likelihood with local
and global priors. Local priors driven by data from seen classes, i.e. classes
that are available at training time, become instrumental in recovering unseen
classes, i.e. classes that are missing at training time, in a generalized ZSL
setting. Hyperparameters of the Bayesian model offer a convenient way to
optimize the trade-off between seen and unseen class accuracy in addition to
guiding other aspects of model fitting. We conduct experiments on seven
benchmark datasets including the large scale ImageNet and show that our model
improves the current state of the art in the challenging generalized ZSL
setting.
