  With recent progress in joint modeling of visual and textual representations,
Vision-Language Pretraining (VLP) has achieved impressive performance on many
multimodal downstream tasks. However, the requirement for expensive annotations
including clean image captions and regional labels limits the scalability of
existing approaches, and complicates the pretraining procedure with the
introduction of multiple dataset-specific objectives. In this work, we relax
these constraints and present a minimalist pretraining framework, named Simple
Visual Language Model (SimVLM). Unlike prior work, SimVLM reduces the training
complexity by exploiting large-scale weak supervision, and is trained
end-to-end with a single prefix language modeling objective. Without utilizing
extra data or task-specific customization, the resulting model significantly
outperforms previous pretraining methods and achieves new state-of-the-art
results on a wide range of discriminative and generative vision-language
benchmarks, including VQA (+3.74% vqa-score), NLVR2 (+1.17% accuracy), SNLI-VE
(+1.37% accuracy) and image captioning tasks (+10.1% average CIDEr score).
Furthermore, we demonstrate that SimVLM acquires strong generalization and
transfer ability, enabling zero-shot behavior including open-ended visual
question answering and cross-modality transfer.
