RIA projects covariance descriptors from the SPD manifold into Euclidean space via Riemannian mappings to preserve structural invariants for VPR, matching supervised zero-shot performance and reaching SOTA with light fine-tuning.
Power Euclidean metrics for covariance matrices with application to diffusion tensor imaging
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abstract
Various metrics for comparing diffusion tensors have been recently proposed in the literature. We consider a broad family of metrics which is indexed by a single power parameter. A likelihood-based procedure is developed for choosing the most appropriate metric from the family for a given dataset at hand. The approach is analogous to using the Box-Cox transformation that is frequently investigated in regression analysis. The methodology is illustrated with a simulation study and an application to a real dataset of diffusion tensor images of canine hearts.
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cs.CV 1years
2026 1verdicts
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Beyond First-Order: Learning Riemannian Geometries for Invariant Visual Place Recognition
RIA projects covariance descriptors from the SPD manifold into Euclidean space via Riemannian mappings to preserve structural invariants for VPR, matching supervised zero-shot performance and reaching SOTA with light fine-tuning.