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Numerous recent works have proposed pretraining generic visio-linguisticrepresentations and then finetuning them for downstream vision and languagetasks. While architecture and objective function design choices have receivedattention, the choice of pretraining datasets has received little attention. Inthis work, we question some of the default choices made in literature. Forinstance, we systematically study how varying similarity between thepretraining dataset domain (textual and visual) and the downstream domainaffects performance. Surprisingly, we show that automatically generated data ina domain closer to the downstream task (e.g., VQA v2) is a better choice forpretraining than "natural" data but of a slightly different domain (e.g.,Conceptual Captions). On the other hand, some seemingly reasonable choices ofpretraining datasets were found to be entirely ineffective for some downstreamtasks. This suggests that despite the numerous recent efforts, vision &language pretraining does not quite work "out of the box" yet. Overall, as aby-product of our study, we find that simple design choices in pretraining canhelp us achieve close to state-of-art results on downstream tasks without anyarchitectural changes.

Are we pretraining it right? Digging deeper into visio-linguistic pretraining