  Vision-and-language (VL) pre-training has proven to be highly effective on
various VL downstream tasks. While recent work has shown that fully
transformer-based VL models can be more efficient than previous
region-feature-based methods, their performance on downstream tasks often
degrades significantly. In this paper, we present METER, a Multimodal
End-to-end TransformER framework, through which we investigate how to design
and pre-train a fully transformer-based VL model in an end-to-end manner.
Specifically, we dissect the model designs along multiple dimensions: vision
encoders (e.g., CLIP-ViT, Swin transformer), text encoders (e.g., RoBERTa,
DeBERTa), multimodal fusion module (e.g., merged attention vs. co-attention),
architectural design (e.g., encoder-only vs. encoder-decoder), and pre-training
objectives (e.g., masked image modeling). We conduct comprehensive experiments
and provide insights on how to train a performant VL transformer. METER
achieves an accuracy of 77.64% on the VQAv2 test-std set using only 4M images
for pre-training, surpassing the state-of-the-art region-feature-based model by
1.04%, and outperforming the previous best fully transformer-based model by
1.6%. Notably, when further scaled up, our best VQA model achieves an accuracy
of 80.54%. Code and pre-trained models are released at
https://github.com/zdou0830/METER.
