37 citations · 146 across the 33 of their papers we have counts for
8 papers · 1 filter
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…
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…
Gramian Multimodal Representation Learning and Alignment
Giordano Cicchetti, Eleonora Grassucci, Luigi Sigillo +1
Human perception integrates multiple modalities, such as vision, hearing, and language, into a unified understanding of the surrounding reality. While recent multimodal models have…
Language-Oriented Semantic Latent Representation for Image Transmission
Giordano Cicchetti, Eleonora Grassucci, Jihong Park +3
In the new paradigm of semantic communication (SC), the focus is on delivering meanings behind bits by extracting semantic information from raw data. Recent advances in data-to-tex…
Towards Explaining Hypercomplex Neural Networks
Eleonora Lopez, Eleonora Grassucci, Debora Capriotti +1
Hypercomplex neural networks are gaining increasing interest in the deep learning community. The attention directed towards hypercomplex models originates from several aspects, spa…
StawGAN: Structural-Aware Generative Adversarial Networks for Infrared Image Translation
Luigi Sigillo, Eleonora Grassucci, Danilo Comminiello
This paper addresses the problem of translating night-time thermal infrared images, which are the most adopted image modalities to analyze night-time scenes, to daytime color image…
Hypercomplex Image-to-Image Translation
Eleonora Grassucci, Luigi Sigillo, Aurelio Uncini +1
Image-to-image translation (I2I) aims at transferring the content representation from an input domain to an output one, bouncing along different target domains. Recent I2I generati…
Multi-View Hypercomplex Learning for Breast Cancer Screening
Eleonora Lopez, Eleonora Grassucci, Danilo Comminiello
Radiologists interpret mammography exams by jointly analyzing all four views, as correlations among them are crucial for accurate diagnosis. Recent methods employ dedicated fusion…