1 citations · 4 across the 7 of their papers we have counts for
7 papers · 1 filter
Generative Uncertainty as a Self-supervised Signal for Semantic Similarity Learning
Enrico Pallotta, Sina Raoufi, Lars Doorenbos +2
Evaluating semantic similarity between videos is a fundamental challenge in computer vision, essential for tasks ranging from out-of-distribution (OOD) detection to video retrieval…
Lightweight 3D Feature Pretraining by Bayesian Inversion of 2D Foundation Models
Marwane Hariat, Gianni Franchi, David Filliat +1
We present Casper3D, a lightweight probabilistic framework for converting noisy multi-view 2D foundation-model embeddings into a latent 3D semantic representation. We model view-le…
Leveraging Visual Signals for Robust Token-Level Uncertainty in Vision-Language Generation
Joseph Hoche, David Brellmann, Gianni Franchi
Uncertainty quantification (UQ) remains a critical challenge in Large Vision Language Models (LVLMs) for reliable predictions and real-world deployment. However, most existing meth…
Organizing Unstructured Image Collections using Natural Language
Mingxuan Liu, Zhun Zhong, Jun Li +3
In this work, we introduce and study the novel task of Open-ended Semantic Multiple Clustering (OpenSMC). Given a large, unstructured image collection, the goal is to automatically…
Frustratingly Easy Test-Time Adaptation of Vision-Language Models
Matteo Farina, Gianni Franchi, Giovanni Iacca +2
Vision-Language Models seamlessly discriminate among arbitrary semantic categories, yet they still suffer from poor generalization when presented with challenging examples. For thi…
Hierarchical Light Transformer Ensembles for Multimodal Trajectory Forecasting
Adrien Lafage, Mathieu Barbier, Gianni Franchi +1
Accurate trajectory forecasting is crucial for the performance of various systems, such as advanced driver-assistance systems and self-driving vehicles. These forecasts allow us to…
How To Effectively Train An Ensemble Of Faster R-CNN Object Detectors To Quantify Uncertainty
Denis Mbey Akola, Gianni Franchi
This paper presents a new approach for training two-stage object detection ensemble models, more specifically, Faster R-CNN models to estimate uncertainty. We propose training one…