Binding Multiple Modalities via Multimodal Wasserstein Barycenter

Abstract:Multimodal learning beyond two modalities commonly leverages a specific modality (e.g., text) to bind other modalities. However, how to establish a more balanced representation space that approximates shared semantics while respecting the holistic geometry of $n$-modal data remains challenging. In this work, we present BaryBind, which aims to transport the specific modality towards the Wasserstein barycenter (WB) optimized across all modalities and introduces a volumetric alignment objective to establish a unified semantic space around the WB embedding. Specifically, we project specific modalities to the WB, which minimizes the average Wasserstein distances to multimodal distributions and serves as the anchor for subsequent alignment. We then construct a barycenter simplex, whose volume is taken as a similarity metric for global alignment centered at the WB. Experiments show that BaryBind achieves competitive performance in text-video-audio retrieval, classification, videoQA, and cross-modal generation tasks, along with robustness under modality absence and scalability to more than three modalities. Code is released at this https URL.

Machine Learning (cs.LG)

arXiv:2609.33800 [cs.LG]

(or arXiv:2609.33800v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.33800 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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