986 citations · 1.1k across the 32 of their papers we have counts for
34 papers
SeeingSounds: Learning Audio-to-Visual Alignment via Text
Simone Carnemolla, Matteo Pennisi, Chiara Russo +3
We introduce SeeingSounds, a lightweight and modular framework for audio-to-image generation that leverages the interplay between audio, language, and vision-without requiring any…
Self-supervised learning for radio-astronomy source classification: a benchmark
Thomas Cecconello, Simone Riggi, Ugo Becciani +5
The upcoming Square Kilometer Array (SKA) telescope marks a significant step forward in radio astronomy, presenting new opportunities and challenges for data analysis. Traditional…
Evidential Federated Learning for Skin Lesion Image Classification
Rutger Hendrix, Federica Proietto Salanitri, Concetto Spampinato +2
We introduce FedEvPrompt, a federated learning approach that integrates principles of evidential deep learning, prompt tuning, and knowledge distillation for distributed skin lesio…
FedRewind: Rewinding Continual Model Exchange for Decentralized Federated Learning
Luca Palazzo, Matteo Pennisi, Federica Proietto Salanitri +3
In this paper, we present FedRewind, a novel approach to decentralized federated learning that leverages model exchange among nodes to address the issue of data distribution shift.…
AIM 2024 Challenge on Video Saliency Prediction: Methods and Results
Andrey Moskalenko, Alexey Bryncev, Dmitry Vatolin +30
This paper reviews the Challenge on Video Saliency Prediction at AIM 2024. The goal of the participants was to develop a method for predicting accurate saliency maps for the provid…
SalFoM: Dynamic Saliency Prediction with Video Foundation Models
Morteza Moradi, Mohammad Moradi, Francesco Rundo +3
Recent advancements in video saliency prediction (VSP) have shown promising performance compared to the human visual system, whose emulation is the primary goal of VSP. However, cu…
Diffexplainer: Towards Cross-modal Global Explanations with Diffusion Models
Matteo Pennisi, Giovanni Bellitto, Simone Palazzo +2
We present DiffExplainer, a novel framework that, leveraging language-vision models, enables multimodal global explainability. DiffExplainer employs diffusion models conditioned on…
Selective Attention-based Modulation for Continual Learning
Giovanni Bellitto, Federica Proietto Salanitri, Matteo Pennisi +5
We present SAM, a biologically-plausible selective attention-driven modulation approach to enhance classification models in a continual learning setting. Inspired by neurophysiolog…
Transformer-based Video Saliency Prediction with High Temporal Dimension Decoding
Morteza Moradi, Simone Palazzo, Concetto Spampinato
In recent years, finding an effective and efficient strategy for exploiting spatial and temporal information has been a hot research topic in video saliency prediction (VSP). With…
Wake-Sleep Consolidated Learning
Amelia Sorrenti, Giovanni Bellitto, Federica Proietto Salanitri +3
We propose Wake-Sleep Consolidated Learning (WSCL), a learning strategy leveraging Complementary Learning System theory and the wake-sleep phases of the human brain to improve the…
MatFuse: Controllable Material Generation with Diffusion Models
Giuseppe Vecchio, Renato Sortino, Simone Palazzo +1
Creating high-quality materials in computer graphics is a challenging and time-consuming task, which requires great expertise. To simplify this process, we introduce MatFuse, a uni…
A Privacy-Preserving Walk in the Latent Space of Generative Models for Medical Applications
Matteo Pennisi, Federica Proietto Salanitri, Giovanni Bellitto +3
Generative Adversarial Networks (GANs) have demonstrated their ability to generate synthetic samples that match a target distribution. However, from a privacy perspective, using GA…
MeT: A Graph Transformer for Semantic Segmentation of 3D Meshes
Giuseppe Vecchio, Luca Prezzavento, Carmelo Pino +3
Polygonal meshes have become the standard for discretely approximating 3D shapes, thanks to their efficiency and high flexibility in capturing non-uniform shapes. This non-uniformi…
On the Effectiveness of Equivariant Regularization for Robust Online Continual Learning
Lorenzo Bonicelli, Matteo Boschini, Emanuele Frascaroli +6
Humans can learn incrementally, whereas neural networks forget previously acquired information catastrophically. Continual Learning (CL) approaches seek to bridge this gap by facil…
BioTrak: A Blockchain-based Platform for Food Chain Logistics Traceability
A. Spitalleri, I. Kavasidis, V. Cartelli +5
The food supply chain, following its globalization, has become very complex. Such complexities, introduce factors that influence adversely the quality of intermediate and final pro…
A baseline on continual learning methods for video action recognition
Giulia Castagnolo, Concetto Spampinato, Francesco Rundo +2
Continual learning has recently attracted attention from the research community, as it aims to solve long-standing limitations of classic supervisedly-trained models. However, most…
Transformer-based Image Generation from Scene Graphs
Renato Sortino, Simone Palazzo, Concetto Spampinato
Graph-structured scene descriptions can be efficiently used in generative models to control the composition of the generated image. Previous approaches are based on the combination…
TinyHD: Efficient Video Saliency Prediction with Heterogeneous Decoders using Hierarchical Maps Distillation
Feiyan Hu, Simone Palazzo, Federica Proietto Salanitri +4
Video saliency prediction has recently attracted attention of the research community, as it is an upstream task for several practical applications. However, current solutions are p…
Transfer without Forgetting
Matteo Boschini, Lorenzo Bonicelli, Angelo Porrello +5
This work investigates the entanglement between Continual Learning (CL) and Transfer Learning (TL). In particular, we shed light on the widespread application of network pretrainin…
Transforming Image Generation from Scene Graphs
Renato Sortino, Simone Palazzo, Concetto Spampinato
Generating images from semantic visual knowledge is a challenging task, that can be useful to condition the synthesis process in complex, subtle, and unambiguous ways, compared to…
Neural Transformers for Intraductal Papillary Mucosal Neoplasms (IPMN) Classification in MRI images
Federica Proietto Salanitri, Giovanni Bellitto, Simone Palazzo +9
Early detection of precancerous cysts or neoplasms, i.e., Intraductal Papillary Mucosal Neoplasms (IPMN), in pancreas is a challenging and complex task, and it may lead to a more f…
Effects of Auxiliary Knowledge on Continual Learning
Giovanni Bellitto, Matteo Pennisi, Simone Palazzo +4
In Continual Learning (CL), a neural network is trained on a stream of data whose distribution changes over time. In this context, the main problem is how to learn new information…
FedER: Federated Learning through Experience Replay and Privacy-Preserving Data Synthesis
Matteo Pennisi, Federica Proietto Salanitri, Giovanni Bellitto +4
In the medical field, multi-center collaborations are often sought to yield more generalizable findings by leveraging the heterogeneity of patient and clinical data. However, recen…
MIDGARD: A Simulation Platform for Autonomous Navigation in Unstructured Environments
Giuseppe Vecchio, Simone Palazzo, Dario C. Guastella +4
We present MIDGARD, an open-source simulation platform for autonomous robot navigation in outdoor unstructured environments. MIDGARD is designed to enable the training of autonomou…