In this paper, we propose an end-to-end Retrieval-Augmented Visual Language Model (REVEAL) that learns to encode world knowledge into a large-scale memory, and to retrieve from it to answer knowledge-intensive queries. REVEAL consists of four key components: the memory, the encoder, the retriever and the generator. The large-scale memory encodes various sources of multimodal world knowledge (e.g. image-text pairs, question answering pairs, knowledge graph triplets, etc) via a unified encoder. The retriever finds the most relevant knowledge entries in the memory, and the generator fuses the retrieved knowledge with the input query to produce the output. A key novelty in our approach is that the memory, encoder, retriever and generator are all pre-trained end-to-end on a massive amount of data. Furthermore, our approach can use a diverse set of multimodal knowledge sources, which is shown to result in significant gains. We show that REVEAL achieves state-of-the-art results on visual question answering and image captioning.

翻译：在本文中，我们提出了一种端到端的检索增强视觉语言模型（REVEAL），该模型学习将世界知识编码到大规模记忆中，并通过检索回答知识密集型查询。REVEAL包含四个关键组件：记忆模块、编码器、检索器和生成器。大规模记忆通过统一编码器编码多种来源的多模态世界知识（例如图像-文本对、问答对、知识图谱三元组等）。检索器在记忆中查找最相关的知识条目，生成器将检索到的知识与输入查询融合以产生输出。我们方法的一项关键创新在于，记忆、编码器、检索器和生成器均在大量数据上进行端到端预训练。此外，我们的方法可利用多样化的多模态知识源，这被证明可带来显著性能提升。实验表明，REVEAL在视觉问答和图像描述任务上达到了最先进水平。