DDGE: Disentangled Dirichlet Geodesic Evaluation for Robust Few-Shot Learning

Recent studies have highlighted the crucial role of distance metrics in noisy few-shot learning. However, existing approaches heavily rely on Euclidean or cosine measurements, failing to recognize the ``deceptive proximity'' where heterogeneous samples from different manifold peaks appear spuriously close in the flat ambient space. In this paper, we present Disentangled Dirichlet Geodesic Evaluation (DDGE), a novel manifold-aware framework that unifies orthogonal disentanglement and conformal integrals to efficiently evaluate complex topological spaces. Specifically, we decouple features into orthogonal semantic subspaces and leverage a prior-guided Dirichlet Process to expand discrete samples into a continuous, noise-purified semantic terrain. Upon this landscape, we measure the intrinsic geodesic distance via a density-aware conformal line integral to capture the authentic class topology. Extensive experiments demonstrate the state-of-the-art performance of DDGE on multiple benchmark datasets.