6 papers
Fast SDP certification of neural networks : towards large multi-class datasets
Margot Boyer, Clément Rambour, Zacharie Alès +1
We present a new quadratic model for the certification problem in adversarial robustness, which simultaneously accounts for all possible target classes. Building on this model, we…
DAFTED: Decoupled Asymmetric Fusion of Tabular and Echocardiographic Data for Cardiac Hypertension Diagnosis
Jérémie Stym-Popper, Nathan Painchaud, Clément Rambour +3
Multimodal data fusion is a key approach for enhancing diagnosis in medical applications. We propose an asymmetric fusion strategy starting from a primary modality and integrating…
CLIPTTA: Robust Contrastive Vision-Language Test-Time Adaptation
Marc Lafon, Gustavo Adolfo Vargas Hakim, Clément Rambour +2
Vision-language models (VLMs) like CLIP exhibit strong zero-shot capabilities but often fail to generalize under distribution shifts. Test-time adaptation (TTA) allows models to up…
ViLU: Learning Vision-Language Uncertainties for Failure Prediction
Marc Lafon, Yannis Karmim, Julio Silva-RodrÃguez +6
Reliable Uncertainty Quantification (UQ) and failure prediction remain open challenges for Vision-Language Models (VLMs). We introduce ViLU, a new Vision-Language Uncertainty quant…
RT-HCP: Dealing with Inference Delays and Sample Efficiency to Learn Directly on Robotic Platforms
Zakariae El Asri, Ibrahim Laiche, Clément Rambour +2
Learning a controller directly on the robot requires extreme sample efficiency. Model-based reinforcement learning (RL) methods are the most sample efficient, but they often suffer…
GalLoP: Learning Global and Local Prompts for Vision-Language Models
Marc Lafon, Elias Ramzi, Clément Rambour +2
Prompt learning has been widely adopted to efficiently adapt vision-language models (VLMs), e.g. CLIP, for few-shot image classification. Despite their success, most prompt learnin…