Building scalable vision-language models to learn from diverse, multimodal data remains an open challenge. In this paper, we introduce an Efficient Vision-languagE foundation model, namely EVE, which is one unified multimodal Transformer pre-trained solely by one unified pre-training task. Specifically, EVE encodes both vision and language within a shared Transformer network integrated with modality-aware sparse Mixture-of-Experts (MoE) modules, which capture modality-specific information by selectively switching to different experts. To unify pre-training tasks of vision and language, EVE performs masked signal modeling on image-text pairs to reconstruct masked signals, i.e., image pixels and text tokens, given visible signals. This simple yet effective pre-training objective accelerates training by 3.5x compared to the model pre-trained with Image-Text Contrastive and Image-Text Matching losses. Owing to the combination of the unified architecture and pre-training task, EVE is easy to scale up, enabling better downstream performance with fewer resources and faster training speed. Despite its simplicity, EVE achieves state-of-the-art performance on various vision-language downstream tasks, including visual question answering, visual reasoning, and image-text retrieval.

翻译：构建可扩展的视觉-语言模型以学习多模态数据仍是开放挑战。本文提出高效视觉-语言基础模型EVE，该模型通过单一统一预训练任务在统一的多模态Transformer框架下完成预训练。具体而言，EVE在共享Transformer网络中集成模态感知的稀疏混合专家（MoE）模块，通过选择性激活不同专家捕获模态特定信息。为实现视觉与语言预训练任务的统一，EVE对图像-文本对执行掩码信号建模——基于可见信号重构被掩码信号（即图像像素与文本标记）。相比采用图像-文本对比损失和图像-文本匹配损失预训练的模型，这一简洁有效的预训练目标将训练速度提升3.5倍。得益于统一架构与预训练任务的结合，EVE易于扩展，能以更少资源实现更快训练速度并获得更优下游性能。尽管设计简约，EVE在视觉问答、视觉推理及图像-文本检索等多个视觉-语言下游任务中均取得最先进性能。