  Zero-shot learning (ZSL) tackles the unseen class recognition problem,
transferring semantic knowledge from seen classes to unseen ones. Typically, to
guarantee desirable knowledge transfer, a common (latent) space is adopted for
associating the visual and semantic domains in ZSL. However, existing common
space learning methods align the semantic and visual domains by merely
mitigating distribution disagreement through one-step adaptation. This strategy
is usually ineffective due to the heterogeneous nature of the feature
representations in the two domains, which intrinsically contain both
distribution and structure variations. To address this and advance ZSL, we
propose a novel hierarchical semantic-visual adaptation (HSVA) framework.
Specifically, HSVA aligns the semantic and visual domains by adopting a
hierarchical two-step adaptation, i.e., structure adaptation and distribution
adaptation. In the structure adaptation step, we take two task-specific
encoders to encode the source data (visual domain) and the target data
(semantic domain) into a structure-aligned common space. To this end, a
supervised adversarial discrepancy (SAD) module is proposed to adversarially
minimize the discrepancy between the predictions of two task-specific
classifiers, thus making the visual and semantic feature manifolds more closely
aligned. In the distribution adaptation step, we directly minimize the
Wasserstein distance between the latent multivariate Gaussian distributions to
align the visual and semantic distributions using a common encoder. Finally,
the structure and distribution adaptation are derived in a unified framework
under two partially-aligned variational autoencoders. Extensive experiments on
four benchmark datasets demonstrate that HSVA achieves superior performance on
both conventional and generalized ZSL. The code is available at
\url{https://github.com/shiming-chen/HSVA} .
