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The goal of zero-shot learning (ZSL) is to recognize objects from unseen classes correctly without corresponding training samples. The existing ZSL methods are trained on a set of predefined classes and do not have the ability to learn from a stream of training data. However, in many real-world applications, training data are collected incrementally; this is one of the main reasons why ZSL methods cannot be applied to certain real-world situations. Accordingly, in order to handle practical learning tasks of this kind, we introduce a novel ZSL setting, referred to as incremental ZSL (IZSL), the goal of which is to accumulate historical knowledge and alleviate Catastrophic Forgetting to facilitate better recognition when incrementally trained on new classes. We further propose a novel method to realize IZSL, which employs a generative replay strategy to produce virtual samples of previously seen classes. The historical …