  The observation that computer vision methods overfit to dataset specifics has
inspired diverse attempts to make object recognition models robust to domain
shifts. However, similar work on domain-robust visual question answering
methods is very limited. Domain adaptation for VQA differs from adaptation for
object recognition due to additional complexity: VQA models handle multimodal
inputs, methods contain multiple steps with diverse modules resulting in
complex optimization, and answer spaces in different datasets are vastly
different. To tackle these challenges, we first quantify domain shifts between
popular VQA datasets, in both visual and textual space. To disentangle shifts
between datasets arising from different modalities, we also construct synthetic
shifts in the image and question domains separately. Second, we test the
robustness of different families of VQA methods (classic two-stream,
transformer, and neuro-symbolic methods) to these shifts. Third, we test the
applicability of existing domain adaptation methods and devise a new one to
bridge VQA domain gaps, adjusted to specific VQA models. To emulate the setting
of real-world generalization, we focus on unsupervised domain adaptation and
the open-ended classification task formulation.
