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Visual question answering (VQA) often requires an understanding of visualconcepts and language semantics, which relies on external knowledge. Mostexisting methods exploit pre-trained language models or/and unstructured text,but the knowledge in these resources are often incomplete and noisy. Some othermethods prefer to use knowledge graphs (KGs) which often have intensivestructured knowledge, but the research is still quite preliminary. In thispaper, we propose LaKo, a knowledge-driven VQA method via LateKnowledge-to-text Injection. To effectively incorporate an external KG, wetransfer triples into textual format and propose a late injection mechanism forknowledge fusion. Finally we address VQA as a text generation task with aneffective encoder-decoder paradigm, which achieves state-of-the-art results onOKVQA dataset.

LaKo: Knowledge-driven Visual Question Answering via Late Knowledge-to-Text Injection