Kavli Affiliate: Biao Huang
| First 5 Authors: Jiaqi Yue, Chunhui Zhao, Jiancheng Zhao, Biao Huang,
| Summary:
Generalized zero-shot learning (GZSL) focuses on recognizing seen and unseen
classes against domain shift problem 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 study cross-domain GZSL (CDGZSL) which
addresses GZSL towards unseen domains. Different from existing GZSL methods,
CDGZSL constructs a common feature space across domains and acquires 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, which eliminates the
non-intrinsic semantics not shared across all domains under the guidance of
inter-class feature relationships, and unseen-class meta generation, 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
two datasets, Office-Home and Mini-DomainNet, and we have shared the LLM-based
semantics for these datasets as a benchmark.
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