37 citations · 146 across the 33 of their papers we have counts for
34 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…
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…
Controllable Audio-Visual Viewpoint Generation from 360° Spatial Information
Christian Marinoni, Riccardo Fosco Gramaccioni, Eleonora Grassucci +1
The generation of sounding videos has seen significant advancements with the advent of diffusion models. However, existing methods often lack the fine-grained control needed to gen…
FoleyGRAM: Video-to-Audio Generation with GRAM-Aligned Multimodal Encoders
Riccardo Fosco Gramaccioni, Christian Marinoni, Eleonora Grassucci +3
In this work, we present FoleyGRAM, a novel approach to video-to-audio generation that emphasizes semantic conditioning through the use of aligned multimodal encoders. Building on…
A TRIANGLE Enables Multimodal Alignment Beyond Cosine Similarity
Giordano Cicchetti, Eleonora Grassucci, Danilo Comminiello
Multimodal learning plays a pivotal role in advancing artificial intelligence systems by incorporating information from multiple modalities to build a more comprehensive representa…
Training-Free Multimodal Guidance for Video to Audio Generation
Eleonora Grassucci, Giuliano Galadini, Giordano Cicchetti +3
Video-to-audio (V2A) generation aims to synthesize realistic and semantically aligned audio from silent videos, with potential applications in video editing, Foley sound design, an…
Semantic Compression via Multimodal Representation Learning
Eleonora Grassucci, Giordano Cicchetti, Aurelio Uncini +1
Multimodal representation learning produces high-dimensional embeddings that align diverse modalities in a shared latent space. While this enables strong generalization, it also in…
Beyond Answers: How LLMs Can Pursue Strategic Thinking in Education
Eleonora Grassucci, Gualtiero Grassucci, Aurelio Uncini +1
Artificial Intelligence (AI) holds transformative potential in education, enabling personalized learning, enhancing inclusivity, and encouraging creativity and curiosity. In this p…
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…
Lightweight Diffusion Models for Resource-Constrained Semantic Communication
Giovanni Pignata, Eleonora Grassucci, Giordano Cicchetti +1
Recently, generative semantic communication models have proliferated as they are revolutionizing semantic communication frameworks, improving their performance, and opening the way…
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…
Demystifying the Hypercomplex: Inductive Biases in Hypercomplex Deep Learning
Danilo Comminiello, Eleonora Grassucci, Danilo P. Mandic +1
Hypercomplex algebras have recently been gaining prominence in the field of deep learning owing to the advantages of their division algebras over real vector spaces and their super…
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…
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…
Semantic Communication Challenges: Understanding Dos and Avoiding Don'ts
Jinho Choi, Jihong Park, Eleonora Grassucci +1
Semantic communication, emerging as a promising paradigm for data transmission, offers an innovative departure from the constraints of Shannon theory, heralding significant advance…
Generative AI Meets Semantic Communication: Evolution and Revolution of Communication Tasks
Eleonora Grassucci, Jihong Park, Sergio Barbarossa +3
While deep generative models are showing exciting abilities in computer vision and natural language processing, their adoption in communication frameworks is still far underestimat…
Hypercomplex Multimodal Emotion Recognition from EEG and Peripheral Physiological Signals
Eleonora Lopez, Eleonora Chiarantano, Eleonora Grassucci +1
Multimodal emotion recognition from physiological signals is receiving an increasing amount of attention due to the impossibility to control them at will unlike behavioral reaction…
Dual Quaternion Rotational and Translational Equivariance in 3D Rigid Motion Modelling
Guilherme Vieira, Eleonora Grassucci, Marcos Eduardo Valle +1
Objects' rigid motions in 3D space are described by rotations and translations of a highly-correlated set of points, each with associated coordinates that real-valued netwo…
PHYDI: Initializing Parameterized Hypercomplex Neural Networks as Identity Functions
Matteo Mancanelli, Eleonora Grassucci, Aurelio Uncini +1
Neural models based on hypercomplex algebra systems are growing and prolificating for a plethora of applications, ranging from computer vision to natural language processing. Hand…
Generalizing Medical Image Representations via Quaternion Wavelet Networks
Luigi Sigillo, Eleonora Grassucci, Aurelio Uncini +1
Neural network generalizability is becoming a broad research field due to the increasing availability of datasets from different sources and for various tasks. This issue is even w…
Attention-Map Augmentation for Hypercomplex Breast Cancer Classification
Eleonora Lopez, Filippo Betello, Federico Carmignani +2
Breast cancer is the most widespread neoplasm among women and early detection of this disease is critical. Deep learning techniques have become of great interest to improve diagnos…
Diffusion models for audio semantic communication
Eleonora Grassucci, Christian Marinoni, Andrea Rodriguez +1
Directly sending audio signals from a transmitter to a receiver across a noisy channel may absorb consistent bandwidth and be prone to errors when trying to recover the transmitted…