Daniela Giordano — 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…

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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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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…

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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 ↗

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 ↗