Visual question answering (VQA) is one of the crucial vision-and-language
tasks. Yet, the bulk of research until recently has focused only on the English
language due to the lack of appropriate evaluation resources. Previous work on
cross-lingual VQA has reported poor zero-shot transfer performance of current
multilingual multimodal Transformers and large gaps to monolingual performance,
attributed mostly to misalignment of text embeddings between the source and
target languages, without providing any additional deeper analyses. In this
work, we delve deeper and address different aspects of cross-lingual VQA
holistically, aiming to understand the impact of input data, fine-tuning and
evaluation regimes, and interactions between the two modalities in
cross-lingual setups. 1) We tackle low transfer performance via novel methods
that substantially reduce the gap to monolingual English performance, yielding
+10 accuracy points over existing transfer methods. 2) We study and dissect
cross-lingual VQA across different question types of varying complexity, across
different multilingual multi-modal Transformers, and in zero-shot and few-shot
scenarios. 3) We further conduct extensive analyses on modality biases in
training data and models, aimed to further understand why zero-shot performance
gaps remain for some question types and languages. We hope that the novel
methods and detailed analyses will guide further progress in multilingual VQA.

该研究探讨了跨语言视觉问答的许多方面，包括输入数据、微调和评估方法，研究了不同多语言多模态变压器下不同问题类型的交互。该研究还针对训练数据和模型进行了广泛的分析，旨在进一步了解为什么在某些问题类型和语言中仍存在零 - shot 效果差距，从而指导多语言 VQA 的进一步发展。