MAP: Multimodal Uncertainty-Aware Vision-Language Pre-training Model

• URL: http://arxiv.org/abs/2210.05335v3
• Date: Thu, 20 Jul 2023 16:24:14 GMT
• Title: MAP: Multimodal Uncertainty-Aware Vision-Language Pre-training Model
• Authors: Yatai Ji, Junjie Wang, Yuan Gong, Lin Zhang, Yanru Zhu, Hongfa Wang, Jiaxing Zhang, Tetsuya Sakai, Yujiu Yang
• Abstract summary: We project the representations of all modalities as probabilistic distributions via a Probability Distribution (PDE) Compared to the existing deterministic methods, such uncertainty modeling can convey richer multimodal semantic information. We propose suitable pre-training tasks: Distribution-based Vision-Language Contrastive learning (D-VLC), Distribution-based Masked Language Modeling (D-MLM), and Distribution-based Image-Text Matching (D-ITM)
• Score: 35.52349231889843
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multimodal semantic understanding often has to deal with uncertainty, which means the obtained messages tend to refer to multiple targets. Such uncertainty is problematic for our interpretation, including inter- and intra-modal uncertainty. Little effort has studied the modeling of this uncertainty, particularly in pre-training on unlabeled datasets and fine-tuning in task-specific downstream datasets. In this paper, we project the representations of all modalities as probabilistic distributions via a Probability Distribution Encoder (PDE) by utilizing sequence-level interactions. Compared to the existing deterministic methods, such uncertainty modeling can convey richer multimodal semantic information and more complex relationships. Furthermore, we integrate uncertainty modeling with popular pre-training frameworks and propose suitable pre-training tasks: Distribution-based Vision-Language Contrastive learning (D-VLC), Distribution-based Masked Language Modeling (D-MLM), and Distribution-based Image-Text Matching (D-ITM). The fine-tuned models are applied to challenging downstream tasks, including image-text retrieval, visual question answering, visual reasoning, and visual entailment, and achieve state-of-the-art results.

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