  A big convergence of language, vision, and multimodal pretraining is
emerging. In this work, we introduce a general-purpose multimodal foundation
model BEiT-3, which achieves state-of-the-art transfer performance on both
vision and vision-language tasks. Specifically, we advance the big convergence
from three aspects: backbone architecture, pretraining task, and model scaling
up. We introduce Multiway Transformers for general-purpose modeling, where the
modular architecture enables both deep fusion and modality-specific encoding.
Based on the shared backbone, we perform masked "language" modeling on images
(Imglish), texts (English), and image-text pairs ("parallel sentences") in a
unified manner. Experimental results show that BEiT-3 obtains state-of-the-art
performance on object detection (COCO), semantic segmentation (ADE20K), image
classification (ImageNet), visual reasoning (NLVR2), visual question answering
(VQAv2), image captioning (COCO), and cross-modal retrieval (Flickr30K, COCO).
