Image As A Foreign Language: Beit Pretraining For All Vision And Vision-language Tasks

Wang Wenhui, Bao Hangbo, Dong Li, Bjorck Johan, Peng Zhiliang, Liu Qiang, Aggarwal Kriti, Mohammed Owais Khan, Singhal Saksham, Som Subhojit, Wei Furu. Arxiv 2022

[Paper]    
Applications Merging Model Architecture Multimodal Models Pretraining Methods Scaling Laws Training Techniques Transformer

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).

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