Deep neural networks continue to advance the state-of-the-art of image recognition tasks with various methods. However, applications of these methods to multimodality remain limited. We present Multimodal Residual Networks (MRN) for the multimodal residual learning of visual question-answering, which extends the idea of the deep residual learning. Unlike the deep residual learning, MRN effectively learns the joint representation from vision and language information. The main idea is to use element-wise multiplication for the joint residual mappings exploiting the residual learning of the attentional models in recent studies. Various alternative models introduced by multimodality are explored based on our study. We achieve the state-of-the-art results on the Visual QA dataset for both Open-Ended and Multiple-Choice tasks. Moreover, we introduce a novel method to visualize the attention effect of the joint representations for each learning block using back-propagation algorithm, even though the visual features are collapsed without spatial information.

介绍了Multimodal Residual Networks（MRN），一种通过联合残差映射学习视觉问答的多模态系统，利用注意力模型的残差学习来将视觉和语言信息联合表示，获得了Visual QA数据集开放式和多项选择任务的最先进结果，并通过反向传播算法引入了一种可视化联合表示的注意力效果的新方法。

多模态残差学习用于视觉问答