Knowing When to Trust Images: Reliability-Aware Multi-modal Entity Alignment

2026-09-19

AI Summary

该研究针对多模态实体对齐中图像噪声和语义错位问题，提出了一种可靠性感知框架RA-MMEA，通过预测图像可靠性和生成稳定视觉嵌入来改善实体对齐效果。

Abstract

The visual modality, i.e., images, plays a key role in multi-modal entity alignment (MMEA). Existing approaches often directly fuse the image with other modalities to align different entities. Although simple, such strategies overlook the potential noise in the images and their semantic misalignment with corresponding entities, resulting in suboptimal fusion and degraded performance. Addressing this, we propose a novel Reliability-Aware framework for MMEA (RA-MMEA), which assesses visual reliability and adaptively improves unreliable visual representations for robust entity alignment. The core lies in two modules, including dependency-aware visual reliability prediction (DA-VRP) and stability-regularized visual embedding generation (SR-VEG). The former aims to estimate the reliability of an image by leveraging multi-modal dependency within the entity, while the latter focuses on producing alternative visual representation conditioned on semantics encoded in textual modalities for multi-modal fusion. Compared to current methods, RA-MMEA enables more reliable visual representations for modality fusion, thereby improving performance. In extensive experiments, RA-MMEA achieves state-of-the-art results, verifying the importance of reliable visual modality for entity alignment and the effectiveness of RA-MMEA. The code and results will be released.

Problem

• multi-modal entity alignment
• visual reliability
• semantic misalignment

Innovation

• Reliability-Aware
• Dependency-Aware Visual Reliability Prediction (DA-VRP)
• Stability-Regularized Visual Embedding Generation (SR-VEG)