A study on zero-shot learning from semantic viewpoint

Recognition of unseen object class by a human being is always based on the relationship between seen and unseen classes, given that human has some background knowledge of the unseen object class. Zero-shot learning is a learning paradigm that tries to develop a recognition model to recognize mutually exclusive training and testing classes. A zero-shot learning model trained on labeled data can also recognize unseen classes when sufficient information about the relationship between seen and unseen classes is given. Semantic space contains semantic information about seen and unseen classes. It is an important part of zero-shot learning and acts as a bridge between seen and unseen classes. In this article, we provide a compact and comprehensive survey on zero-shot learning. First, we explain the different ways to construct semantic space along with its pros and cons. Next, we present a categorization of zero-shot learning methods from the semantic space construction point of view. Furthermore, this paper also presents performance evaluation measures with a relevant and influential zero-shot learning database.