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.

本研究通过量化视觉问答（VQA）数据集在视觉和文本空间上的领域漂移，构建并测试不同家族的 VQA 方法（经典的两流，Transformer 和神经符号方法）的鲁棒性以及现有的领域自适应方法的适用性，并设计了一种新的跨越 VQA 领域差距的自适应方法。