  In the generalized zero-shot learning, synthesizing unseen data with
generative models has been the most popular method to address the imbalance of
training data between seen and unseen classes. However, this method requires
that the unseen semantic information is available during the training stage,
and training generative models is not trivial. Given that the generator of
these models can only be trained with seen classes, we argue that synthesizing
unseen data may not be an ideal approach for addressing the domain shift caused
by the imbalance of the training data. In this paper, we propose to realize the
generalized zero-shot recognition in different domains. Thus, unseen (seen)
classes can avoid the effect of the seen (unseen) classes. In practice, we
propose a threshold and probabilistic distribution joint method to segment the
testing instances into seen, unseen and uncertain domains. Moreover, the
uncertain domain is further adjusted to alleviate the domain shift. Extensive
experiments on five benchmark datasets show that the proposed method exhibits
competitive performance compared with that based on generative models.
