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cs.CV2025
Learning Eigenstructures of Unstructured Data Manifolds
Roy Velich, Arkadi Piven, David Bensaïd +3
We introduce a novel framework that directly learns a spectral basis for shape and manifold analysis from unstructured data, eliminating the need for traditional operator selection…
cs.CV2025
FlowFeat: Pixel-Dense Embedding of Motion Profiles
Nikita Araslanov, Anna Sonnweber, Daniel Cremers
Dense and versatile image representations underpin the success of virtually all computer vision applications. However, state-of-the-art networks, such as transformers, produce low-…
cs.CV2025
Finsler Multi-Dimensional Scaling: Manifold Learning for Asymmetric Dimensionality Reduction and Embedding
Thomas Dagès, Simon Weber, Ya-Wei Eileen Lin +5
Dimensionality reduction is a fundamental task that aims to simplify complex data by reducing its feature dimensionality while preserving essential patterns, with core applications…