activity
20162026
most citedGroup Sparse Regularization for Deep Neural Networks

477 citations · 819 across the 59 of their papers we have counts for

collaborators
Showing 2024Show all

17 papers · 1 filter

cs.SD2024

FolAI: Synchronized Foley Sound Generation with Semantic and Temporal Alignment

Riccardo Fosco Gramaccioni, Christian Marinoni, Emilian Postolache +4

Traditional sound design workflows rely on manual alignment of audio events to visual cues, as in Foley sound design, where everyday actions like footsteps or object interactions a…

cs.CV2024

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…

cs.LG2024

Hierarchical Hypercomplex Network for Multimodal Emotion Recognition

Eleonora Lopez, Aurelio Uncini, Danilo Comminiello

Emotion recognition is relevant in various domains, ranging from healthcare to human-computer interaction. Physiological signals, being beyond voluntary control, offer reliable inf…

eess.SP2024★ 7 cited

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…

eess.IV2024★ 1 cited

A Wavelet Diffusion GAN for Image Super-Resolution

Lorenzo Aloisi, Luigi Sigillo, Aurelio Uncini +1

In recent years, diffusion models have emerged as a superior alternative to generative adversarial networks (GANs) for high-fidelity image generation, with wide applications in tex…

cs.CV2024

Guess What I Think: Streamlined EEG-to-Image Generation with Latent Diffusion Models

Eleonora Lopez, Luigi Sigillo, Federica Colonnese +2

Generating images from brain waves is gaining increasing attention due to its potential to advance brain-computer interface (BCI) systems by understanding how brain signals encode…