VL-BEiT: Generative Vision-Language Pretraining

Hangbo Bao; Wenhui Wang; Li Dong; Furu Wei

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arXiv · 2206.01127

VL-BEiT: Generative Vision-Language Pretraining

Hangbo Bao·Wenhui Wang·Li Dong·Furu Wei

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Abstract

We introduce a vision-language foundation model called VL-BEiT, which is a bidirectional multimodal Transformer learned by generative pretraining. Our minimalist solution conducts masked prediction on both monomodal and multimodal data with a shared Transformer. Specifically, we perform masked vision-language modeling on image-text pairs, masked language modeling on texts, and masked image modeling on images. VL-BEiT is learned from scratch with one unified pretraining task, one shared backbone, and one-stage training. Our method is conceptually simple and empirically effective. Experimental results show that VL-BEiT obtains strong results on various vision-language benchmarks, such as visual question answering, visual reasoning, and image-text retrieval. Moreover, our method learns transferable visual features, achieving competitive performance on image classification, and semantic segmentation.

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Hangbo Bao, Wenhui Wang, Li Dong, Furu Wei. 2022-06-02. VL-BEiT: Generative Vision-Language Pretraining. https://arxiv.org/abs/2206.01127

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