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cs.CV2026

Benchmarking Hyperspectral Foundation Models for Hyperspectral Unmixing

Edgard Dabier, Christophe Kervazo, Pietro Gori +1

Several foundation models dedicated to hyperspectral images have recently been made available. These models are trained on large unlabeled datasets and exhibit strong performance o…

cs.LG2026

Geometric-to-Semantic Spherical Transfer Learning for Cortical Sulci Labeling

Saeb Tounsi, Joël Chavas, Pietro Gori +3

Deep learning on cortical surfaces faces a dilemma: capturing the complex topology of over 60 nomenclature-dependent sulci per hemisphere requires high-capacity models, yet the ext…

q-bio.QM2026

Representation learning of human cortical folding to reveal long lasting neurodevelopmental signatures

Julien Laval, Robin Guiavarch, Antoine Dufournet +24

The human brain folds in utero, primarily during late gestation. Shortly after birth, cortical folding patterns are established and remain stable thereafter, making them promising…

cs.CV2026

Counterfactual Contrastive Analysis

Yunlong He, Pietro Gori

Visual Counterfactual Explanations (VCEs) aim to explain image classifiers by generating minimally edited and realistic versions of an input image that change the classifier's pred…

cs.CV2026

Diff-CA: Separating Common and Salient Factors with Diffusion Models

Michaël Soumm, Alexandre Fournier Montgieux, Yunlong He +2

Contrastive Analysis aims to separate factors that are common between two data distributions from those that are salient to only one of them. Existing contrastive methods are based…

cs.LG2026

Automatic Discovery of Disease Subgroups by Contrasting with Healthy Controls

Robin Louiset, Edouard Duchesnay, Benoit Dufumier +2

In biomedical Subgroup Discovery, practitioners are interested in discovering interpretable and homogeneous subgroups within a group of patients. In this paper, assuming that healt…