  Currently, domestic service robots have an insufficient ability to interact
naturally through language. This is because understanding human instructions is
complicated by various ambiguities and missing information. In existing
methods, the referring expressions that specify the relationships between
objects are insufficiently modeled. In this paper, we propose Target-dependent
UNITER, which learns the relationship between the target object and other
objects directly by focusing on the relevant regions within an image, rather
than the whole image. Our method is an extension of the UNITER-based
Transformer that can be pretrained on general-purpose datasets. We extend the
UNITER approach by introducing a new architecture for handling the target
candidates. Our model is validated on two standard datasets, and the results
show that Target-dependent UNITER outperforms the baseline method in terms of
classification accuracy.
