activity
20242026
collaborators

9 papers

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…

eess.IV2026

Streamlined Hybrid Annotation Framework using Scalable Codestream for Bandwidth-Restricted UAV Object Detection

Karim El Khoury, Tiffanie Godelaine, Simon Delvaux +2

Emergency response missions depend on the fast relay of visual information, a task to which unmanned aerial vehicles are well adapted. However, the effective use of unmanned aerial…

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.SD2026

Leveraging Prediction Entropy for Automatic Prompt Weighting in Zero-Shot Audio-Language Classification

Karim El Khoury, Maxime Zanella, Tiffanie Godelaine +2

Audio-language models have recently demonstrated strong zero-shot capabilities by leveraging natural-language supervision to classify audio events without labeled training data. Ye…

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,…

cs.LG2025

Optimizing Resources for On-the-Fly Label Estimation with Multiple Unknown Medical Experts

Tim Bary, Tiffanie Godelaine, Axel Abels +1

Accurate ground truth estimation in medical screening programs often relies on coalitions of experts and peer second opinions. Algorithms that efficiently aggregate noisy annotatio…