针对多模态知识图谱少样本关系学习，提出自适应融合框架提升泛化能力。
FusionAdapter for Few-Shot Relation Learning in Multimodal Knowledge Graphs
- 引入适配器模块，高效适配各模态应对未见关系
- 设计保留模态特性的融合策略，提升少样本性能
- 适合多模态知识图谱中低资源关系建模任务
多模态知识图谱（MMKG）通过文本与图像等多源信息增强实体和关系表征。同一实体的不同模态常提供互补且异质的信息。然而，现有方法主要将各模态对齐至统一空间，忽视了特定模态的独特贡献，尤其在低资源场景下表现受限。为此，本文提出用于少样本关系学习（FSRL）的FusionAdapter：(1) 设计适配器模块，使各模态可高效适应未见关系；(2) 提出融合策略，在整合多模态实体表征的同时保留模态特异性。通过有效适配与融合，该方法显著提升对新关系的泛化能力，仅需极少监督。在两个基准MMKG数据集上的实验表明，FusionAdapter优于当前最优方法。
原文摘要 · Abstract (English)
Multimodal Knowledge Graphs (MMKGs) incorporate various modalities, including text and images, to enhance entity and relation representations. Notably, different modalities for the same entity often present complementary and diverse information. However, existing MMKG methods primarily align modalities into a shared space, which tends to overlook the distinct contributions of specific modalities, limiting their performance particularly in low-resource settings. To address this challenge, we propose FusionAdapter for the learning of few-shot relationships (FSRL) in MMKG. FusionAdapter introduces (1) an adapter module that enables efficient adaptation of each modality to unseen relations and (2) a fusion strategy that integrates multimodal entity representations while preserving diverse modality-specific characteristics. By effectively adapting and fusing information from diverse modalities, FusionAdapter improves generalization to novel relations with minimal supervision. Extensive experiments on two benchmark MMKG datasets demonstrate that FusionAdapter achieves superior performance over state-of-the-art methods.
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