Graph4MM: Weaving Multimodal Learning with Structural Information

• URL: http://arxiv.org/abs/2510.16990v1
• Date: Sun, 19 Oct 2025 20:13:03 GMT
• Title: Graph4MM: Weaving Multimodal Learning with Structural Information
• Authors: Xuying Ning, Dongqi Fu, Tianxin Wei, Wujiang Xu, Jingrui He,
• Abstract summary: Graphs provide powerful structural information for modeling intra- and inter-modal relationships.<n>Previous works fail to distinguish multi-hop neighbors and treat the graph as a standalone modality.<n>We propose Graph4MM, a graph-based multimodal learning framework.
• Score: 52.16646463590474
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Real-world multimodal data usually exhibit complex structural relationships beyond traditional one-to-one mappings like image-caption pairs. Entities across modalities interact in intricate ways, with images and text forming diverse interconnections through contextual dependencies and co-references. Graphs provide powerful structural information for modeling intra-modal and inter-modal relationships. However, previous works fail to distinguish multi-hop neighbors and treat the graph as a standalone modality, which fragments the overall understanding. This limitation presents two key challenges in multimodal learning: (1) integrating structural information from multi-hop neighbors into foundational models, and (2) fusing modality-specific information in a principled manner. To address these challenges, we revisit the role of graphs in multimodal learning within the era of foundation models and propose Graph4MM, a graph-based multimodal learning framework. To be specific, we introduce Hop-Diffused Attention, which integrates multi-hop structural information into self-attention through causal masking and hop diffusion. Furthermore, we design MM-QFormer, a multi-mapping querying transformer for cross-modal fusion. Through theoretical and empirical analysis, we show that leveraging structures to integrate both intra- and inter-modal interactions improves multimodal understanding beyond treating them as a standalone modality. Experiments on both generative and discriminative tasks show that Graph4MM outperforms larger VLMs, LLMs, and multimodal graph baselines, achieving a 6.93% average improvement.

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