Image as a Foreign Language: BEiT Pretraining for Vision and Vision-Language Tasks
Wenhui Wang, Hangbo Bao, Li Dong, Johan Bjorck, Zhiliang Peng, Qiang Liu, Kriti Aggarwal, Owais Khan Mohammed
Abstract
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 excellent 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 use 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 remarkable 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).
BibTeX
@inproceedings{cvpr2023_imageasaforeignl,
title = {Image as a Foreign Language: BEiT Pretraining for Vision and Vision-Language Tasks},
author = {Wenhui Wang and Hangbo Bao and Li Dong and Johan Bjorck and Zhiliang Peng and Qiang Liu and Kriti Aggarwal and Owais Khan Mohammed and Saksham Singhal and Subhojit Som and Furu Wei},
booktitle = {CVPR 2023},
year = {2023}
}