GOMA: Toward Structure-Driven Multimodal Alignment from a Graph Signal Smoothing Perspective
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Computer Science > Machine Learning
arXiv:2605.15723 (cs)
[Submitted on 15 May 2026]
Title:GOMA: Toward Structure-Driven Multimodal Alignment from a Graph Signal Smoothing Perspective
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Abstract:Multimodal alignment is commonly learned from isolated image-text pairs via CLIP-style dual encoders, leaving the relational context among entities largely unused. Multimodal attributed graphs (MAGs), where nodes carry multimodal attributes and edges encode corpus structure, provide a natural setting for refining frozen vision-language embeddings. This refinement is challenging: visual, textual, and cross-modal relations often induce different neighborhood geometries, while unrestricted graph propagation can quickly over-smooth retrieval representations. Effectively leveraging graph context therefore requires simultaneously breaking modality-specific topological barriers, controlling the smoothing regime, and preserving informative smoothing before semantic boundaries collapse. We propose Graph-Optimized Multimodal Alignment (GOMA), a structure-driven post-alignment framework that views frozen multimodal embeddings as graph signals and addresses these requirements through a unified retrieval-oriented design. GOMA decouples three key design choices: where messages should flow, how multimodal evidence should propagate, and which smoothing depth should be retained. Concretely, it learns modality-aware propagation operators, performs finite-step coupled smoothing without diagonal cross-modal shortcuts, and adaptively reads out node-specific smoothing trajectories to preserve useful smoothing before collapse. All experiments follow a transductive MAG retrieval protocol where the graph serves only as unlabeled context and diagonal self-pair edges are removed. On seven MAG benchmarks, GOMA achieves state-of-the-art or tied state-of-the-art retrieval and remains substantially more stable than the strongest graph competitor, demonstrating that MAG structure can serve as an effective post-encoder for frozen multimodal embeddings.
| Subjects: | Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2605.15723 [cs.LG] |
| (or arXiv:2605.15723v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.15723
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled GOMA: Toward Structure-Driven Multimodal Alignment from a Graph Signal Smoothing Perspective, by Xu Wang and 4 other authors
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