We propose Dual Attention Networks (DANs) which jointly leverage visual and
textual attention mechanisms to capture fine-grained interplay between vision
and language. DANs attend to specific regions in images and words in text
through multiple steps and gather essential information from both modalities.
Based on this framework, we introduce two types of DANs for multimodal
reasoning and matching, respectively. The reasoning model allows visual and
textual attentions to steer each other during collaborative inference, which is
useful for tasks such as Visual Question Answering (VQA). In addition, the
matching model exploits the two attention mechanisms to estimate the similarity
between images and sentences by focusing on their shared semantics. Our
extensive experiments validate the effectiveness of DANs in combining vision
and language, achieving the state-of-the-art performance on public benchmarks
for VQA and image-text matching.

提出双重关注网络框架（Dual Attention Networks），应用于视觉和文本场景的多模态推理、比对任务，在 Visual Question Answering 和图片 - 文本比对任务中取得最新的优异表现。