Poster

VLMo: Unified Vision-Language Pre-Training with Mixture-of-Modality-Experts

Hangbo Bao · Wenhui Wang · Li Dong · Qiang Liu · Owais Khan Mohammed · Kriti Aggarwal · Subhojit Som · Songhao Piao · Furu Wei

Hall J (level 1) #635

Abstract:

We present a unified Vision-Language pretrained Model (VLMo) that jointly learns a dual encoder and a fusion encoder with a modular Transformer network. Specifically, we introduce Multiway Transformer, where each block contains a pool of modality-specific experts and a shared self-attention layer. Because of the modeling flexibility of Multiway Transformer, pretrained VLMo can be fine-tuned as a fusion encoder for vision-language classification tasks, or used as a dual encoder for efficient image-text retrieval. Moreover, we propose a stagewise pre-training strategy, which effectively leverages large-scale image-only and text-only data besides image-text pairs. Experimental results show that VLMo achieves state-of-the-art results on various vision-language tasks, including VQA, NLVR2 and image-text retrieval.

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