  Generalized zero-shot learning (GZSL) focuses on recognizing seen and unseen
classes against domain shift problem (DSP) where data of unseen classes may be
misclassified as seen classes. However, existing GZSL is still limited to seen
domains. In the current work, we pioneer cross-domain GZSL (CDGZSL) which
addresses GZSL towards unseen domains. Different from existing GZSL methods
which alleviate DSP by generating features of unseen classes with semantics,
CDGZSL needs to construct a common feature space across domains and acquire the
corresponding intrinsic semantics shared among domains to transfer from seen to
unseen domains. Considering the information asymmetry problem caused by
redundant class semantics annotated with large language models (LLMs), we
present Meta Domain Alignment Semantic Refinement (MDASR). Technically, MDASR
consists of two parts: Inter-class Similarity Alignment (ISA), which eliminates
the non-intrinsic semantics not shared across all domains under the guidance of
inter-class feature relationships, and Unseen-class Meta Generation (UMG),
which preserves intrinsic semantics to maintain connectivity between seen and
unseen classes by simulating feature generation. MDASR effectively aligns the
redundant semantic space with the common feature space, mitigating the
information asymmetry in CDGZSL. The effectiveness of MDASR is demonstrated on
the Office-Home and Mini-DomainNet, and we have shared the LLM-based semantics
for these datasets as the benchmark.
