13 citations · 41 across the 14 of their papers we have counts for
14 papers
Improving Semantic Uncertainty Quantification in LVLMs with Semantic Gaussian Processes
Joseph Hoche, Andrei Bursuc, David Brellmann +4
Large Vision-Language Models (LVLMs) often produce plausible but unreliable outputs, making robust uncertainty estimation essential. Recent work on semantic uncertainty estimates r…
Make Me a BNN: A Simple Strategy for Estimating Bayesian Uncertainty from Pre-trained Models
Gianni Franchi, Olivier Laurent, Maxence Leguéry +3
Deep Neural Networks (DNNs) are powerful tools for various computer vision tasks, yet they often struggle with reliable uncertainty quantification - a critical requirement for real…
The Robust Semantic Segmentation UNCV2023 Challenge Results
Xuanlong Yu, Yi Zuo, Zitao Wang +34
This paper outlines the winning solutions employed in addressing the MUAD uncertainty quantification challenge held at ICCV 2023. The challenge was centered around semantic segment…
Improving CLIP Robustness with Knowledge Distillation and Self-Training
Clement Laroudie, Andrei Bursuc, Mai Lan Ha +1
This paper examines the robustness of a multi-modal computer vision model, CLIP (Contrastive Language-Image Pretraining), in the context of unsupervised learning. The main objectiv…
Learning to Generate Training Datasets for Robust Semantic Segmentation
Marwane Hariat, Olivier Laurent, Rémi Kazmierczak +4
Semantic segmentation methods have advanced significantly. Still, their robustness to real-world perturbations and object types not seen during training remains a challenge, partic…
Packed-Ensembles for Efficient Uncertainty Estimation
Olivier Laurent, Adrien Lafage, Enzo Tartaglione +4
Deep Ensembles (DE) are a prominent approach for achieving excellent performance on key metrics such as accuracy, calibration, uncertainty estimation, and out-of-distribution detec…