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Outside-knowledge visual question answering (OK-VQA) requires the agent tocomprehend the image, make use of relevant knowledge from the entire web, anddigest all the information to answer the question. Most previous works addressthe problem by first fusing the image and question in the multi-modal space,which is inflexible for further fusion with a vast amount of externalknowledge. In this paper, we call for a paradigm shift for the OK-VQA task,which transforms the image into plain text, so that we can enable knowledgepassage retrieval, and generative question-answering in the natural languagespace. This paradigm takes advantage of the sheer volume of gigantic knowledgebases and the richness of pre-trained language models. ATransform-Retrieve-Generate framework (TRiG) framework is proposed, which canbe plug-and-played with alternative image-to-text models and textual knowledgebases. Experimental results show that our TRiG framework outperforms allstate-of-the-art supervised methods by at least 11.1% absolute margin.

A Thousand Words Are Worth More Than a Picture: Natural Language-Centric Outside-Knowledge Visual Question Answering