A structured diffusion bridge method achieves near fully-paired modality translation quality using alignment constraints even in unpaired or semi-paired regimes.
Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=
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Derives novel generalization error bounds for multimodal pairwise metric learning showing that fine-grained modality features reduce hypothesis space complexity via enhanced complementarity.
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Structured Diffusion Bridges: Inductive Bias for Denoising Diffusion Bridges
A structured diffusion bridge method achieves near fully-paired modality translation quality using alignment constraints even in unpaired or semi-paired regimes.
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Quantifying Multimodal Capabilities: Formal Generalization Guarantees in Pairwise Metric Learning
Derives novel generalization error bounds for multimodal pairwise metric learning showing that fine-grained modality features reduce hypothesis space complexity via enhanced complementarity.