Concetto Spampinato — accepted AI-conference papers

2025

DEXTER: Diffusion-Guided EXplanations with TExtual Reasoning for Vision Models

Simone Carnemolla, Matteo Pennisi, Sarinda Samarasinghe, Giovanni Bellitto, Simone Palazzo, Daniela Giordano +2

NeurIPS 2025spotlight

Understanding and explaining the behavior of machine learning models is essential for building transparent and trustworthy AI systems. We introduce DEXTER, a data-free framework that employs diffusion models and large language models to generate global, textual explanations of visual classifiers. D…

Cited by 0 Source ↗code ↗Scholar ↗

2025

Distilling Knowledge from Large Video Models for Driver Visual Attention Prediction

Morteza Moradi, Mohammad Moradi, Concetto Spampinato, Ali Borji, Simone Palazzo

ICASSP 2025accepted

Driver attention prediction has gained significant attention recently due to its role in developing advanced driver assistance systems (ADAS) and intelligent vehicles. The emergence of video foundation models (VFMs) has opened up new possibilities for improving video understanding tasks like video s…

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2025

EEG-Music Emotion Recognition: Challenge Overview

Salvatore Calcagno, Simone Carnemolla, Isaak Kavasidis, Simone Palazzo, Daniela Giordano, Concetto Spampinato

ICASSP 2025accepted

As our understanding of emotions continues to evolve, the ability of machines to accurately interpret and respond to emotional cues is more important than ever. Traditional methods of emotion recognition often fall short, particularly when it comes to the subtle and complex responses elicited by mus…

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2024

Domain Generalization with fourier Transform and soft thresholding

Hongyi Pan, Bin Wang, Zheyuan Zhang, Xin Zhu, Debesh Jha, Ahmet Enis Çetin +2

ICASSP 2024accepted

Domain generalization aims to train models on multiple source domains so that they can generalize well to unseen target domains. Among many domain generalization methods, Fourier-transformbased domain generalization methods have gained popularity primarily because they exploit the power of Fourier t…

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2024

MatFuse: Controllable Material Generation with Diffusion Models

Giuseppe Vecchio, Renato Sortino, Simone Palazzo, Concetto Spampinato

CVPR 2024poster

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 unified approach that harnesses the generative power of diffusion models for creation and editing of 3D materials. Our method…

Cited by 23 Source ↗PDF ↗code ↗Scholar ↗

2024

Saliency-driven Experience Replay for Continual Learning

Giovanni Bellitto, Federica Proietto Salanitri, Matteo Pennisi, Matteo Boschini, Lorenzo Bonicelli, Angelo Porrello +3

NeurIPS 2024spotlight

We present Saliency-driven Experience Replay - SER - a biologically-plausible approach based on replicating human visual saliency to enhance classification models in continual learning settings. Inspired by neurophysiological evidence that the primary visual cortex does not contribute to object mani…

Cited by 3 Source ↗PDF ↗code ↗Scholar ↗

2023

Ensemble and Personalized Transformer Models for Subject Identification and Relapse Detection in E-Prevention Challenge

Salvatore Calcagno, Raffaele Mineo, Daniela Giordano, Concetto Spampinato

ICASSP 2023accepted

In this short paper, we present the devised solutions for the subject identification and relapse detection tasks, which are part of the e-Prevention Challenge hosted at the ICASSP 2023 conference [1] [2] [3]. We specifically design an ensemble scheme of six models - five transformer-based ones and a…

Cited by 0 Source ↗Scholar ↗

2022

On the Effectiveness of Lipschitz-Driven Rehearsal in Continual Learning

Lorenzo Bonicelli, Matteo Boschini, Angelo Porrello, Concetto Spampinato, Simone Calderara

NeurIPS 2022accept

Rehearsal approaches enjoy immense popularity with Continual Learning (CL) practitioners. These methods collect samples from previously encountered data distributions in a small memory buffer; subsequently, they repeatedly optimize on the latter to prevent catastrophic forgetting. This work draws at…

Cited by 55 Source ↗PDF ↗code ↗Scholar ↗

2022

Transfer without Forgetting

Matteo Boschini, Lorenzo Bonicelli, Angelo Porrello, Giovanni Bellitto, Matteo Pennisi, Simone Palazzo +2

ECCV 2022poster

"This work investigates the entanglement between Continual Learning (CL) and Transfer Learning (TL). In particular, we shed light on the widespread application of network pretraining, highlighting that it is itself subject to catastrophic forgetting. Unfortunately, this issue leads to the under-expl…

Cited by 57 Source ↗PDF ↗code ↗Scholar ↗

2021

SurfaceNet: Adversarial SVBRDF Estimation From a Single Image

Giuseppe Vecchio, Simone Palazzo, Concetto Spampinato

ICCV 2021poster

In this paper we present SurfaceNet, an approach for estimating spatially-varying bidirectional reflectance distribution function (SVBRDF) material properties from a single image. We pose the problem as an image translation task and propose a novel patch-based generative adversarial network (GAN) th…

Cited by 38 PDF ↗code ↗Scholar ↗

2020

Domain Adaptation for Outdoor Robot Traversability Estimation from RGB data with Safety-Preserving Loss

Simone Palazzo, Dario C. Guastella, Luciano Cantelli, Paolo Spadaro, Francesco Rundo, Giovanni Muscato +2

IROS 2020poster

Being able to estimate the traversability of the area surrounding a mobile robot is a fundamental task in the design of a navigation algorithm. However, the task is often complex, since it requires evaluating distances from obstacles, type and slope of terrain, and dealing with non-obvious discontin…

Cited by 39 Source ↗Scholar ↗

2017

Deep Learning Human Mind for Automated Visual Classification

Concetto Spampinato, Simone Palazzo, Isaak Kavasidis, Daniela Giordano, Nasim Souly, Mubarak Shah

CVPR 2017oral

What if we could effectively read the mind and transfer human visual capabilities to computer vision methods? In this paper, we aim at addressing this question by developing the first visual object classifier driven by human brain signals. In particular, we employ EEG data evoked by visual object st…

Cited by 319 PDF ↗Scholar ↗

2017

Generative Adversarial Networks Conditioned by Brain Signals

Simone Palazzo, Concetto Spampinato, Isaak Kavasidis, Daniela Giordano, Mubarak Shah

ICCV 2017poster

Recent advancements in generative adversarial networks (GANs), using deep convolutional models, have supported the development of image generation techniques able to reach satisfactory levels of realism. Further improvements have been proposed to condition GANs to generate images matching a specific…

Cited by 134 PDF ↗Scholar ↗

2017

Semi Supervised Semantic Segmentation Using Generative Adversarial Network

Nasim Souly, Concetto Spampinato, Mubarak Shah

ICCV 2017poster

Semantic segmentation has been a long standing challenging task in computer vision. It aims at assigning a label to each image pixel and needs a significant number of pixel-level annotated data, which is often unavailable. To address this lack of annotations, in this paper, we leverage, on one hand,…

Cited by 589 PDF ↗Scholar ↗

2015

Superpixel-Based Video Object Segmentation Using Perceptual Organization and Location Prior

Daniela Giordano, Francesca Murabito, Simone Palazzo, Concetto Spampinato

CVPR 2015poster

In this paper we present an approach for segmenting objects in videos taken in complex scenes with multiple and different targets. The method does not make any specific assumptions about the videos and relies on how objects are perceived by humans according to Gestalt laws. Initially, we rapidly gen…

Cited by 99 Source ↗PDF ↗Scholar ↗