Learning effective fusion of multi-modality features is at the heart of
visual question answering. We propose a novel method of dynamically fusing
multi-modal features with intra- and inter-modality information flow, which
alternatively pass dynamic information between and across the visual and
language modalities. It can robustly capture the high-level interactions
between language and vision domains, thus significantly improves the
performance of visual question answering. We also show that the proposed
dynamic intra-modality attention flow conditioned on the other modality can
dynamically modulate the intra-modality attention of the target modality, which
is vital for multimodality feature fusion. Experimental evaluations on the VQA
2.0 dataset show that the proposed method achieves state-of-the-art VQA
performance. Extensive ablation studies are carried out for the comprehensive
analysis of the proposed method.

提出了动态融合多模态特征的方法，在视觉和语言模式之间和内部模型之间传递动态信息，从而捕捉语言和视觉领域之间的高级交互作用，显著提高视觉问题回答的效率，同时还提出了关于多模态特征融合的联合训练方法，并使用 VQA 2.0 数据集进行了实验验证，取得了最佳表现。