Outside-knowledge visual question answering is a challenging task that requires both the acquisition and the use of open-ended real-world knowledge. Some existing solutions draw external knowledge into the cross-modality space which overlooks the much vaster textual knowledge in natural-language space, while others transform the image into a text that further fuses with the textual knowledge into the natural-language space and completely abandons the use of visual features. In this paper, we are inspired to constrain the cross-modality space into the same space of natural-language space which makes the visual features preserved directly, and the model still benefits from the vast knowledge in natural-language space. To this end, we propose a novel framework consisting of a multimodal encoder, a textual encoder and an answer decoder. Such structure allows us to introduce more types of knowledge including explicit and implicit multimodal and textual knowledge. Extensive experiments validate the superiority of the proposed method which outperforms the state-of-the-art by 6.17% accuracy. We also conduct comprehensive ablations of each component, and systematically study the roles of varying types of knowledge. Codes and knowledge data can be found at https://github.com/PhoebusSi/Thinking-while-Observing.

翻译：外部知识视觉问答是一项具有挑战性的任务，需要获取并使用开放式的现实世界知识。现有的一些解决方案将外部知识引入跨模态空间，忽略了自然语言空间中更为丰富的文本知识；而另一些方法则将图像转换为文本，进一步与文本知识融合到自然语言空间中，完全放弃了视觉特征的使用。在本文中，我们受到启发，将跨模态空间约束到与自然语言空间相同的空间中，这样既能直接保留视觉特征，又能使模型受益于自然语言空间中的海量知识。为此，我们提出了一种包含多模态编码器、文本编码器和答案解码器的新框架。这种结构允许我们引入更多类型的知识，包括显式和隐式的多模态与文本知识。大量实验验证了所提出方法的优越性，其准确率比现有最佳方法高出6.17%。我们还对每个组件进行了全面的消融研究，并系统性地分析了不同类型知识的作用。代码和知识数据可在 https://github.com/PhoebusSi/Thinking-while-Observing 获取。