  In this paper we propose a framework for predicting kernelized classifiers in
the visual domain for categories with no training images where the knowledge
comes from textual description about these categories. Through our optimization
framework, the proposed approach is capable of embedding the class-level
knowledge from the text domain as kernel classifiers in the visual domain. We
also proposed a distributional semantic kernel between text descriptions which
is shown to be effective in our setting. The proposed framework is not
restricted to textual descriptions, and can also be applied to other forms
knowledge representations. Our approach was applied for the challenging task of
zero-shot learning of fine-grained categories from text descriptions of these
categories.
