  The need to address the scarcity of task-specific annotated data has resulted
in concerted efforts in recent years for specific settings such as zero-shot
learning (ZSL) and domain generalization (DG), to separately address the issues
of semantic shift and domain shift, respectively. However, real-world
applications often do not have constrained settings and necessitate handling
unseen classes in unseen domains -- a setting called Zero-shot Domain
Generalization, which presents the issues of domain and semantic shifts
simultaneously. In this work, we propose a novel approach that learns
domain-agnostic structured latent embeddings by projecting images from
different domains as well as class-specific semantic text-based representations
to a common latent space. In particular, our method jointly strives for the
following objectives: (i) aligning the multimodal cues from visual and
text-based semantic concepts; (ii) partitioning the common latent space
according to the domain-agnostic class-level semantic concepts; and (iii)
learning a domain invariance w.r.t the visual-semantic joint distribution for
generalizing to unseen classes in unseen domains. Our experiments on the
challenging DomainNet and DomainNet-LS benchmarks show the superiority of our
approach over existing methods, with significant gains on difficult domains
like quickdraw and sketch.
