1 citations · 1 across the 9 of their papers we have counts for
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Trust in Vision-Language Models: Insights from a Participatory User Workshop
Agnese Chiatti, Lara Piccolo, Sara Bernardini +2
With the growing deployment of Vision-Language Models (VLMs), pre-trained on large image-text and video-text datasets, it is critical to equip users with the tools to discern when…
AgriGS-SLAM: Orchard Mapping Across Seasons via Multi-View Gaussian Splatting SLAM
Mirko Usuelli, David Rapado-Rincon, Gert Kootstra +1
Autonomous robots in orchards require real-time 3D scene understanding despite repetitive row geometry, seasonal appearance changes, and wind-driven foliage motion. We present Agri…
SCENEFORGE: Enhancing 3D-text alignment with Structured Scene Compositions
Cristian Sbrolli, Matteo Matteucci
The whole is greater than the sum of its parts-even in 3D-text contrastive learning. We introduce SceneForge, a novel framework that enhances contrastive alignment between 3D point…
Mapping User Trust in Vision Language Models: Research Landscape, Challenges, and Prospects
Agnese Chiatti, Sara Bernardini, Lara Shibelski Godoy Piccolo +2
The rapid adoption of Vision Language Models (VLMs), pre-trained on large image-text and video-text datasets, calls for protecting and informing users about when to trust these sys…
Your Image Generator Is Your New Private Dataset
Nicolo Resmini, Eugenio Lomurno, Cristian Sbrolli +1
Generative diffusion models have emerged as powerful tools to synthetically produce training data, offering potential solutions to data scarcity and reducing labelling costs for do…
Neuro-Symbolic Scene Graph Conditioning for Synthetic Image Dataset Generation
Giacomo Savazzi, Eugenio Lomurno, Cristian Sbrolli +2
As machine learning models increase in scale and complexity, obtaining sufficient training data has become a critical bottleneck due to acquisition costs, privacy constraints, and…