  Visual question answering (VQA) is the multi-modal task of answering natural
language questions about an input image. Through cross-dataset adaptation
methods, it is possible to transfer knowledge from a source dataset with larger
train samples to a target dataset where training set is limited. Suppose a VQA
model trained on one dataset train set fails in adapting to another, it is hard
to identify the underlying cause of domain mismatch as there could exists a
multitude of reasons such as image distribution mismatch and question
distribution mismatch. At UCLA, we are working on a VQG module that facilitate
in automatically generating OOD shifts that aid in systematically evaluating
cross-dataset adaptation capabilities of VQA models.
