GOMA refines frozen multimodal embeddings via modality-aware graph signal smoothing on attributed graphs to improve retrieval while avoiding over-smoothing.
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cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
R-GFM constructs multi-scale Riemannian graph-of-graphs to learn geometry-adaptive representations, reducing structural domain generalization error and delivering up to 49% relative gains on downstream graph tasks.
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GOMA: Toward Structure-Driven Multimodal Alignment from a Graph Signal Smoothing Perspective
GOMA refines frozen multimodal embeddings via modality-aware graph signal smoothing on attributed graphs to improve retrieval while avoiding over-smoothing.
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Learning Graph Foundation Models on Riemannian Graph-of-Graphs
R-GFM constructs multi-scale Riemannian graph-of-graphs to learn geometry-adaptive representations, reducing structural domain generalization error and delivering up to 49% relative gains on downstream graph tasks.