  Existing techniques for image-to-image translation commonly have suffered
from two critical problems: heavy reliance on per-sample domain annotation
and/or inability of handling multiple attributes per image. Recent
truly-unsupervised methods adopt clustering approaches to easily provide
per-sample one-hot domain labels. However, they cannot account for the
real-world setting: one sample may have multiple attributes. In addition, the
semantics of the clusters are not easily coupled to the human understanding. To
overcome these, we present a LANguage-driven Image-to-image Translation model,
dubbed LANIT. We leverage easy-to-obtain candidate attributes given in texts
for a dataset: the similarity between images and attributes indicates
per-sample domain labels. This formulation naturally enables multi-hot label so
that users can specify the target domain with a set of attributes in language.
To account for the case that the initial prompts are inaccurate, we also
present prompt learning. We further present domain regularization loss that
enforces translated images be mapped to the corresponding domain. Experiments
on several standard benchmarks demonstrate that LANIT achieves comparable or
superior performance to existing models.
