  Inspired by strategies like Active Learning, it is intuitive that
intelligently selecting the training classes from a dataset for Zero-Shot
Learning (ZSL) can improve the performance of existing ZSL methods. In this
work, we propose a framework called Diverse and Rare Class Identifier (DiRaC-I)
which, given an attribute-based dataset, can intelligently yield the most
suitable "seen classes" for training ZSL models. DiRaC-I has two main goals -
constructing a diversified set of seed classes, followed by a visual-semantic
mining algorithm initialized by these seed classes that acquires the classes
capturing both diversity and rarity in the object domain adequately. These
classes can then be used as "seen classes" to train ZSL models for image
classification. We adopt a real-world scenario where novel object classes are
available to neither DiRaC-I nor the ZSL models during training and conducted
extensive experiments on two benchmark data sets for zero-shot image
classification - CUB and SUN. Our results demonstrate DiRaC-I helps ZSL models
to achieve significant classification accuracy improvements.
