Multimodal representation learning has shown promising improvements on various vision-language tasks. Most existing methods excel at building global-level alignment between vision and language while lacking effective fine-grained image-text interaction. In this paper, we propose a jointly masked multimodal modeling method to learn fine-grained multimodal representations. Our method performs joint masking on image-text input and integrates both implicit and explicit targets for the masked signals to recover. The implicit target provides a unified and debiased objective for vision and language, where the model predicts latent multimodal representations of the unmasked input. The explicit target further enriches the multimodal representations by recovering high-level and semantically meaningful information: momentum visual features of image patches and concepts of word tokens. Through such a masked modeling process, our model not only learns fine-grained multimodal interaction, but also avoids the semantic gap between high-level representations and low- or mid-level prediction targets (e.g. image pixels), thus producing semantically rich multimodal representations that perform well on both zero-shot and fine-tuned settings. Our pre-trained model (named MAMO) achieves state-of-the-art performance on various downstream vision-language tasks, including image-text retrieval, visual question answering, visual reasoning, and weakly-supervised visual grounding.

翻译：多模态表征学习在各类视觉-语言任务中已展现出显著的性能提升。现有方法大多擅长构建视觉与语言之间的全局对齐，但缺乏有效的细粒度图像-文本交互。本文提出一种联合掩码多模态建模方法，用于学习细粒度多模态表征。该方法对图像-文本输入执行联合掩码操作，并集成隐式与显式两类目标以恢复被掩码信号。隐式目标为视觉与语言提供统一且去偏的目标函数，使模型预测未掩码输入的潜在多模态表征；显式目标则通过恢复高层语义信息（图像块的动量视觉特征与词元的概念表征）进一步丰富多模态表征。通过此掩码建模过程，模型不仅能够学习细粒度多模态交互，还避免了高层表征与中低层预测目标（如图像像素）之间的语义鸿沟，从而生成语义丰富的多模态表征，在零样本与微调设置下均表现优异。预训练模型MAMO在图像-文本检索、视觉问答、视觉推理及弱监督视觉定位等下游视觉-语言任务中均达到最优性能。