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This paper introduces Riemannian Wasserstein Entropic Flow Matching (RWEFM), a generative framework for modeling probability distributions on Riemannian manifolds, with applications in scientific domains like single-cell biology and protein conformations.
MC-RFM proposes a novel Riemannian flow-matching framework for few-shot adaptation that models feature displacement on a mixed-curvature manifold combining hyperbolic and Euclidean spaces, outperforming existing methods across multiple visual recognition benchmarks.