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Outside-Knowledge Visual Question Answering (OK-VQA) is a challenging VQAtask that requires retrieval of external knowledge to answer questions aboutimages. Recent OK-VQA systems use Dense Passage Retrieval (DPR) to retrievedocuments from external knowledge bases, such as Wikipedia, but with DPRtrained separately from answer generation, introducing a potential limit on theoverall system performance. Instead, we propose a joint training scheme whichincludes differentiable DPR integrated with answer generation so that thesystem can be trained in an end-to-end fashion. Our experiments show that ourscheme outperforms recent OK-VQA systems with strong DPR for retrieval. We alsointroduce new diagnostic metrics to analyze how retrieval and generationinteract. The strong retrieval ability of our model significantly reduces thenumber of retrieved documents needed in training, yielding significant benefitsin answer quality and computation required for training.

Retrieval Augmented Visual Question Answering with Outside Knowledge