UG-UMRE: Uncertainty-Guided Modality Augmentation and Distributional Calibration for Unified Multimodal Relation Extraction

• URL: http://arxiv.org/abs/2608.04949v1
• Date: Wed, 05 Aug 2026 15:18:16 GMT
• Title: UG-UMRE: Uncertainty-Guided Modality Augmentation and Distributional Calibration for Unified Multimodal Relation Extraction
• Abstract summary: Uncertainty-Driven Unimodal Augmentation (UDUA) and Joint Aleatoric Uncertainty Alignment (JAUA) are proposed.<n>UDUA effectively filters out noise while maintaining semantic consistency.<n>JAUAleverages probabilistic distribution consistency to construct a shared latent space.<n>Experiments on three benchmark datasets demonstrate that UG-UMRE achieves state-of-the-art performance.
• Score: 15.235667200083226
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Unified Multimodal Relation Extraction (UMRE) aims to identify intra-modal and cross-modal relations between textual entities and visual objects. However, existing UMRE studies still encounter two critical issues: ignoring inherent aleatoric uncertainty causes noise propagation, and deep-seated heterogeneity between distinct modal distributions hinders alignment. To address these issues, we propose the Uncertainty-Guided UMRE Network (UG-UMRE). Specifically, we design an Uncertainty-Driven Unimodal Augmentation (UDUA) module, which models features as Gaussian distributions based on the Variational Information Bottleneck. By incorporating an uncertainty-aware self-supervised contrastive learning mechanism, UDUA effectively filters out noise while maintaining semantic consistency. Furthermore, we introduce the Joint Aleatoric Uncertainty Alignment (JAUA) module as a global semantic pre-calibration mechanism. JAUA leverages probabilistic distribution consistency to construct a shared latent space, eliminating the distributional gap by synchronizing cross-modal statistical properties, thereby laying a robust foundation for fine-grained interaction. Experiments on three benchmark datasets (UMRE, MORE, and MNRE) demonstrate that UG-UMRE achieves state-of-the-art performance. Further analysis validates the pluggable and effective performance of the proposed UDUA and JAUA modules.

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