We propose a Retrieval-Augmented Visual Language
Model (REVEAL), which learns to use knowledge from dif-
ferent sources for solving knowledge-intensive tasks. For
both pre-training and fine-tuning, our goal is to learn the
distribution P (y | x) to generate a textual output y condi-
tioned on a multimodal input query x. REVEAL contains
REVEAL-Retrieval-AugmentedVisual-LanguagePre-Trainingwith Multi-SourceMultimodalKnowledgeMemory