GeoTTER: Leveraging Local Geometry of Optimal Transport for Zero-Shot Classification

arXiv:2609.13518v1 Announce Type: new Abstract: We present GeoTTER, a novel framework that redefines optimal transport in the realm of zero-shot classification. Conventional methods often suffer from miscalibration and a lack of adaptability, as they rely on fixed cost matrices derived solely from pre-trained model embeddings. In contrast, GeoTTER addresses these limitations by incorporating two key techniques. First, to alleviate high-frequency label jaggedness (sample-level manifold jitter that assigns neighboring embeddings to different classes), GeoTTER integrates local geometric structure into the optimal transport formulation via graph-Laplacian smoothing, a technique grounded in spectral graph theory that enforces neighborhood consistency. Second, to correct coherent angular drift (a low-frequency orientation bias in which large groups of samples share the same angular offset from their true label prototypes), we fuse clustering-guided cost components with a globally adjusted transport cost, achieving a multi-objective optimization that respects both global distribution constraints and latent data structure. With a median improvement of +6.82% compared to zero-shot and +2.13% compared to OTTER, GeoTTER shows robust improvements across a diverse set of benchmarks.

Related

• Density-Reweighted Entropic Optimal Transport: Decoupling Geometry from Sampling Density
• Pair-Centric Graph Rewiring for Over-Squashing via Optimal Transport-Guided Communication Alignment
• SCOT: Multi-Source Cross-City Transfer with Optimal-Transport Soft-Correspondence Objective

Source: arXiv cs.LG | 2026-09-15