  Transductive zero-shot learning (T-ZSL) which could alleviate the domain
shift problem in existing ZSL works, has received much attention recently.
However, an open problem in T-ZSL: how to effectively make use of unseen-class
samples for training, still remains. Addressing this problem, we first
empirically analyze the roles of unseen-class samples with different degrees of
hardness in the training process based on the uneven prediction phenomenon
found in many ZSL methods, resulting in three observations. Then, we propose
two hardness sampling approaches for selecting a subset of diverse and hard
samples from a given unseen-class dataset according to these observations. The
first one identifies the samples based on the class-level frequency of the
model predictions while the second enhances the former by normalizing the class
frequency via an approximate class prior estimated by an explored prior
estimation algorithm. Finally, we design a new Self-Training framework with
Hardness Sampling for T-ZSL, called STHS, where an arbitrary inductive ZSL
method could be seamlessly embedded and it is iteratively trained with
unseen-class samples selected by the hardness sampling approach. We introduce
two typical ZSL methods into the STHS framework and extensive experiments
demonstrate that the derived T-ZSL methods outperform many state-of-the-art
methods on three public benchmarks. Besides, we note that the unseen-class
dataset is separately used for training in some existing transductive
generalized ZSL (T-GZSL) methods, which is not strict for a GZSL task. Hence,
we suggest a more strict T-GZSL data setting and establish a competitive
baseline on this setting by introducing the proposed STHS framework to T-GZSL.
