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

• URL: http://arxiv.org/abs/2111.02358v1
• Date: Wed, 3 Nov 2021 17:20:36 GMT
• Title: VLMo: Unified Vision-Language Pre-Training with Mixture-of-Modality-Experts
• Authors: Wenhui Wang, Hangbo Bao, Li Dong, Furu Wei
• Abstract summary: We present a unified Vision-Language pretrained Model (VLMo) that jointly learns a dual encoder and a fusion encoder with a modular Transformer network. Because of the modeling flexibility of MoME, pretrained VLMo can be fine-tuned as a fusion encoder for vision-language classification tasks. We propose a stagewise pre-training strategy, which effectively leverages large-scale image-only and text-only data besides image-text pairs.
• Score: 46.55920956687346
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 Mixture-of-Modality-Experts (MoME) Transformer, where each block contains a pool of modality-specific experts and a shared self-attention layer. Because of the modeling flexibility of MoME, 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 and NLVR2. The code and pretrained models are available at https://aka.ms/vlmo.

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