Multimodal Alignment Through Joint Kernel Entropic Gromov--Wasserstein Optimal Transport
We study the problem of aligning data from multiple modalities into a shared representation space, focusing on settings where strong pretrained unimodal encoders are available but cross-modal paired data are scarce. We propose a structure-preserving alignment framework, joint kernel entropic Gromov--Wasserstein Optimal Transport (JK-EGW), which maps multiple modalities into a common latent space by minimizing a quadratic optimal transport objective. JK-EGW leverages fine-grained similarity relationships within and across modalities to construct a global affinity kernel instead of relying on raw feature-space distances. Our framework naturally provides explicit control over the geometry an...
| Authors | Yixuan Florence Wu, Yilun Zhu, Naichen Shi |
|---|---|
| Primary category | math.ST |
| Published | 4 August 2026 |
| Last updated at source | 4 August 2026 |
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