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cs.CV2026

Locally Consistent Transductive Information Maximization for Few-Shot Remote Sensing Scene Classification

Karim El Khoury, Benoît Gérin, Benoît Macq +1

Remote sensing scene classification is increasingly relying on foundation models pre-trained on large-scale Earth-observation data. Moreover, transductive inference, which exploits…

cs.CV2026

CAD-Free Learning of Spacecraft Pose Estimators via NeRF-Based Augmentations

Antoine Legrand, Renaud Detry, Christophe De Vleeschouwer

Spacecraft pose estimation networks require tens of thousands of CAD-rendered images to be trained. This reliance on synthetic CAD data (i) limits applicability to targets with rel…

cs.CV2026

NeRF-based Spacecraft Reconstruction from Monocular Imagery Under Illumination Variability and Pose Uncertainty

Antoine Legrand, Renaud Detry, Christophe De Vleeschouwer

Autonomous rendezvous and proximity operations around uncooperative, unknown spacecraft are critical for active debris removal and on-orbit servicing missions. A key component of s…

cs.CV2026

Conditional Random Fields for Interactive Refinement of Histopathological Predictions

Tiffanie Godelaine, Maxime Zanella, Karim El Khoury +3

Assisting pathologists in the analysis of histopathological images has high clinical value, as it supports cancer detection and staging. In this context, histology foundation model…

cs.CV2025

NeRF-based Visualization of 3D Cues Supporting Data-Driven Spacecraft Pose Estimation

Antoine Legrand, Renaud Detry, Christophe De Vleeschouwer

On-orbit operations require the estimation of the relative 6D pose, i.e., position and orientation, between a chaser spacecraft and its target. While data-driven spacecraft pose es…

cs.CV2025

Few-Shot Adaptation Benchmark for Remote Sensing Vision-Language Models

Karim El Khoury, Maxime Zanella, Christophe De Vleeschouwer +1

Remote Sensing Vision-Language Models (RSVLMs) have shown remarkable potential thanks to large-scale pretraining, achieving strong zero-shot performance on various tasks. However,…