2 citations · 2 across the 7 of their papers we have counts for
9 papers
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…
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…
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…
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…
Can Shape-Infused Joint Embeddings Improve Image-Conditioned 3D Diffusion?
Cristian Sbrolli, Paolo Cudrano, Matteo Matteucci
Recent advancements in deep generative models, particularly with the application of CLIP (Contrastive Language Image Pretraining) to Denoising Diffusion Probabilistic Models (DDPMs…
IC3D: Image-Conditioned 3D Diffusion for Shape Generation
Cristian Sbrolli, Paolo Cudrano, Matteo Frosi +1
In recent years, Denoising Diffusion Probabilistic Models (DDPMs) have demonstrated exceptional performance in various 2D generative tasks. Following this success, DDPMs have been…