Reproduce “Graph4MM: Weaving Multimodal Learning with Structural Information” on OpenResearch
Turn your favorite coding agents into research agents that review literature, develop hypotheses, and run experiments.
Universal app for Apple silicon and Intel. Requires macOS 11 or later.
About this paper
Xuying Ning, Dongqi Fu, Tianxin Wei, Wujiang Xu, Jingrui He
Graph4MM presents a framework that integrates complex structural information from multimodal graphs directly into foundation models to enhance their understanding of intricate "many-to-many" relationships. The system achieves state-of-the-art performance across generative and discriminative tasks, outperforming existing VLMs, LLMs, and graph-based baselines by an average of 6.93%.
Key insights
- Treating graph structure as a standalone modality creates a "semantic gap" with foundation models; instead, graphs should guide intra-modal and inter-modal interactions.
- Multi-hop structural information, when properly integrated via mechanisms like Hop-Diffused Attention, is crucial for robust multimodal understanding and significantly improves performance in complex contexts.
- Hop-Diffused Attention is provably less prone to over-smoothing than traditional GAT layers, preserving feature variance during aggregation while effectively incorporating global connectivity.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.