with the trade-off of a huge computational cost. REVEAL
achieves higher performance than those methods without
relying on such large language models3. Compared with
the previous state-of-the-art, KAT and ReVIVE, which also
utilizes T5-Large as a generator, REVEAL achieves accuracy
of 59.1%, which is +6.0% higher than the single KAT [13]
model and +2.5% higher than ReVIVE [24].

On A-OKVQA, REVEAL achieves 52.2% accuracy,
which is +3.6% higher than the previous best, GPV-2 [21].
We also show two examples of these datasets in Figure 4. All
these results show that, with proper end-to-end retrieval train-
ing and a diverse set of knowledge sources, REVEAL can
learn to retrieve meaningful knowledge entries, and achieve
promising results without relying on a large language model.

5.2. Evaluating on Image Captioning
REVEAL-Retrieval-AugmentedVisual-LanguagePre-Trainingwith Multi-SourceMultimodalKnowledgeMemory