1 citations · 1 across the 9 of their papers we have counts for
14 papers
Following Delegation Traces to Study Human Oversight in Interaction with Multimodal AI
Agnese Chiatti, Matteo Matteucci, Viola Schiaffonati
As multimodal AI systems are becoming our everyday thought partners, the shift towards studying how users transfer control to these systems becomes especially urgent. In this paper…
ArborSplat: Online Semantic Gaussian Splatting SLAM for Orchards
Alessandro Masini, Matteo Frosi, Mirko Usuelli +1
Orchard robots need maps that preserve small but semantically important structures such as trunks, trellises, and fruit. 3D Gaussian Splatting (3DGS) SLAM achieves high photometric…
Knowledge-Guided 3D CT Generation: A Conditioning-Centric Taxonomy
Francesca Pia Panaccione, Eugenio Lomurno, Matteo Matteucci
Controllable generation guided by external knowledge is a key requirement in modern generative deep learning applications, enabling the synthesis of samples with explicit constrain…
Synesthesia via Direct Latent Augmentation:Bypassing the Decode-Encode Loop for Cross-Modal Distillation
Cristian Sbrolli, Nicolas Michel, Matteo Matteucci +1
While multimodal integration significantly improves computer vision models, deploying them incurs prohibitive inference costs and requires scarce, perfectly paired datasets. Recent…
Evidence-Based Text-Conditioned 3D CT Synthesis for Ovarian Cancer
Francesca Pia Panaccione, Eugenio Lomurno, Francesca Fati +11
Ovarian cancer is frequently diagnosed at an advanced stage, making preoperative contrast-enhanced computed tomography (CT) central to staging and surgical planning; yet the scarci…
Beyond Bag-of-Words: Diagnosing Compositional Binding Failures in Vision-Language Models
Cristian Sbrolli, Matteo Matteucci, Toshihiko Yamasaki
Modern vision-language models struggle with basic compositional reasoning, failing to bind attributes to objects or relations to their referents. Existing benchmarks either rely on…
PolyGen: Fully Synthetic Vision-Language Training via Multi-Generator Ensembles
Leonardo Brusini, Cristian Sbrolli, Eugenio Lomurno +2
Synthetic data offers a scalable solution for vision-language pre-training, yet current state-of-the-art methods typically rely on scaling up a single generative backbone, which in…
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…
No Captions, No Problem: Captionless 3D-CLIP Alignment with Hard Negatives via CLIP Knowledge and LLMs
Cristian Sbrolli, Matteo Matteucci
In this study, we explore an alternative approach to enhance contrastive text-image-3D alignment in the absence of textual descriptions for 3D objects. We introduce two unsupervise…