Bayesian Zero-Shot Learning

Abstract

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 available at training time, become instrumental in recovering unseen classes, i.e., classes that are missing at training time, in a generalized ZSL (GZSL) setting. Hyperparameters of the Bayesian model offer a convenient way to optimize the trade-off between seen and unseen class accuracy. We conduct experiments on seven benchmark datasets, including a large scale ImageNet and show that our model produces promising results in the challenging GZSL setting.