  Generalized zero shot learning (GZSL) is defined by a training process
containing a set of visual samples from seen classes and a set of semantic
samples from seen and unseen classes, while the testing process consists of the
classification of visual samples from seen and unseen classes. Current
approaches are based on testing processes that focus on only one of the
modalities (visual or semantic), even when the training uses both modalities
(mostly for regularizing the training process). This under-utilization of
modalities, particularly during testing, can hinder the classification accuracy
of the method. In addition, we note a scarce attention to the development of
learning methods that explicitly optimize a balanced performance of seen and
unseen classes. Such issue is one of the reasons behind the vastly superior
classification accuracy of seen classes in GZSL methods. In this paper, we
mitigate these issues by proposing a new GZSL method based on multi-modal
training and testing processes, where the optimization explicitly promotes a
balanced classification accuracy between seen and unseen classes. Furthermore,
we explore Bayesian inference for the visual and semantic classifiers, which is
another novelty of our work in the GZSL framework. Experiments show that our
method holds the state of the art (SOTA) results in terms of harmonic mean
(H-mean) classification between seen and unseen classes and area under the seen
and unseen curve (AUSUC) on several public GZSL benchmarks.
