EVE: Efficient Vision-Language Pre-training with Masked Prediction and Modality-Aware MoE

• URL: http://arxiv.org/abs/2308.11971v2
• Date: Fri, 1 Mar 2024 11:22:54 GMT
• Title: EVE: Efficient Vision-Language Pre-training with Masked Prediction and Modality-Aware MoE
• Authors: Junyi Chen, Longteng Guo, Jia Sun, Shuai Shao, Zehuan Yuan, Liang Lin, Dongyu Zhang
• Abstract summary: Efficient Vision-languagE is one unified multimodal Transformer pre-trained solely by one unified pre-training task. Eve encodes both vision and language within a shared Transformer network integrated with modality-aware sparse Mixture-of-Experts. Eve achieves state-of-the-art performance on various vision-language downstream tasks, including visual question answering, visual reasoning, and image-text retrieval.
• Score: 66.48689706116808
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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