  For effective human-robot collaboration, it is crucial for robots to
understand requests from users and ask reasonable follow-up questions when
there are ambiguities. While comprehending the users' object descriptions in
the requests, existing studies have focused on this challenge for limited
object categories that can be detected or localized with existing object
detection and localization modules. On the other hand, in the wild, it is
impossible to limit the object categories that can be encountered during the
interaction. To understand described objects and resolve ambiguities in the
wild, for the first time, we suggest a method by leveraging explainability. Our
method focuses on the active regions of a scene to find the described objects
without putting the previous constraints on object categories and natural
language instructions. We evaluate our method in varied real-world images and
observe that the regions suggested by our method can help resolve ambiguities.
When we compare our method with a state-of-the-art baseline, we show that our
method performs better in scenes with ambiguous objects which cannot be
recognized by existing object detectors.
