  Zero-shot learning (ZSL) aims to recognize instances of unseen classes solely
based on the semantic descriptions of the classes. Existing algorithms usually
formulate it as a semantic-visual correspondence problem, by learning mappings
from one feature space to the other. Despite being reasonable, previous
approaches essentially discard the highly precious discriminative power of
visual features in an implicit way, and thus produce undesirable results. We
instead reformulate ZSL as a conditioned visual classification problem, i.e.,
classifying visual features based on the classifiers learned from the semantic
descriptions. With this reformulation, we develop algorithms targeting various
ZSL settings: For the conventional setting, we propose to train a deep neural
network that directly generates visual feature classifiers from the semantic
attributes with an episode-based training scheme; For the generalized setting,
we concatenate the learned highly discriminative classifiers for seen classes
and the generated classifiers for unseen classes to classify visual features of
all classes; For the transductive setting, we exploit unlabeled data to
effectively calibrate the classifier generator using a novel
learning-without-forgetting self-training mechanism and guide the process by a
robust generalized cross-entropy loss. Extensive experiments show that our
proposed algorithms significantly outperform state-of-the-art methods by large
margins on most benchmark datasets in all the ZSL settings. Our code is
available at \url{https://github.com/kailigo/cvcZSL}
