  Visual question answering (VQA) often requires an understanding of visual
concepts and language semantics, which relies on external knowledge. Most
existing methods exploit pre-trained language models or/and unstructured text,
but the knowledge in these resources are often incomplete and noisy. Some other
methods prefer to use knowledge graphs (KGs) which often have intensive
structured knowledge, but the research is still quite preliminary. In this
paper, we propose LaKo, a knowledge-driven VQA method via Late
Knowledge-to-text Injection. To effectively incorporate an external KG, we
transfer triples into textual format and propose a late injection mechanism for
knowledge fusion. Finally we address VQA as a text generation task with an
effective encoder-decoder paradigm, which achieves state-of-the-art results on
OKVQA dataset.
