15 papers
Trimodal Glioma Representation Alignment via Volumetric Contrastive Learning
Denise Marini, Eleonora Grassucci, Danilo Comminiello
Glioma grading and survival prediction require the integration of heterogeneous information collected at different spatial and biological scales. Histopathology describes tissue mo…
GRAMformer: Any-Order Modality Interactions via Volumetric Multimodal Cross-Attention
Giordano Cicchetti, Eleonora Grassucci, Danilo Comminiello
Transformer-based multimodal models rely on attention mechanisms to integrate information across heterogeneous modalities. Despite their success, existing multimodal attention form…
Training-Free Multi-User Generative Semantic Communications via Null-Space Diffusion Sampling
Eleonora Grassucci, Jinho Choi, Jihong Park +3
In recent years, novel communication strategies have emerged to face the challenges that the increased number of connected devices and the higher quality of transmitted information…
Closing the gap in multimodal medical representation alignment
Eleonora Grassucci, Giordano Cicchetti, Danilo Comminiello
In multimodal learning, CLIP has emerged as the de-facto approach for mapping different modalities into a shared latent space by bringing semantically similar representations close…
Closing the Modality Gap Aligns Group-Wise Semantics
Eleonora Grassucci, Giordano Cicchetti, Emanuele Frasca +2
In multimodal learning, CLIP has been recognized as the \textit{de facto} method for learning a shared latent space across multiple modalities, placing similar representations clos…
Generative Semantic Communication: Diffusion Models Beyond Bit Recovery
Eleonora Grassucci, Sergio Barbarossa, Danilo Comminiello
Semantic communication is expected to be one of the cores of next-generation AI-based communications. One of the possibilities offered by semantic communication is the capability t…