136 citations · 421 across the 32 of their papers we have counts for
32 papers
Second FRCSyn-onGoing: Winning Solutions and Post-Challenge Analysis to Improve Face Recognition with Synthetic Data
Ivan DeAndres-Tame, Ruben Tolosana, Pietro Melzi +56
Synthetic data is gaining increasing popularity for face recognition technologies, mainly due to the privacy concerns and challenges associated with obtaining real data, including…
LIMBA: An Open-Source Framework for the Preservation and Valorization of Low-Resource Languages using Generative Models
Salvatore Mario Carta, Stefano Chessa, Giulia Contu +19
Minority languages are vital to preserving cultural heritage, yet they face growing risks of extinction due to limited digital resources and the dominance of artificial intelligenc…
The Impact of Balancing Real and Synthetic Data on Accuracy and Fairness in Face Recognition
Andrea Atzori, Pietro Cosseddu, Gianni Fenu +1
Over the recent years, the advancements in deep face recognition have fueled an increasing demand for large and diverse datasets. Nevertheless, the authentic data acquired to creat…
How Fair is Your Diffusion Recommender Model?
Daniele Malitesta, Giacomo Medda, Erasmo Purificato +3
Diffusion-based learning has settled as a rising paradigm in generative recommendation, outperforming traditional approaches built upon variational autoencoders and generative adve…
Fair Augmentation for Graph Collaborative Filtering
Ludovico Boratto, Francesco Fabbri, Gianni Fenu +2
Recent developments in recommendation have harnessed the collaborative power of graph neural networks (GNNs) in learning users' preferences from user-item networks. Despite emergin…
If It's Not Enough, Make It So: Reducing Authentic Data Demand in Face Recognition through Synthetic Faces
Andrea Atzori, Fadi Boutros, Naser Damer +2
Recent advances in deep face recognition have spurred a growing demand for large, diverse, and manually annotated face datasets. Acquiring authentic, high-quality data for face rec…
Second Edition FRCSyn Challenge at CVPR 2024: Face Recognition Challenge in the Era of Synthetic Data
Ivan DeAndres-Tame, Ruben Tolosana, Pietro Melzi +55
Synthetic data is gaining increasing relevance for training machine learning models. This is mainly motivated due to several factors such as the lack of real data and intra-class v…
Robustness in Fairness against Edge-level Perturbations in GNN-based Recommendation
Ludovico Boratto, Francesco Fabbri, Gianni Fenu +2
Efforts in the recommendation community are shifting from the sole emphasis on utility to considering beyond-utility factors, such as fairness and robustness. Robustness of recomme…
A Cost-Sensitive Meta-Learning Strategy for Fair Provider Exposure in Recommendation
Ludovico Boratto, Giulia Cerniglia, Mirko Marras +2
When devising recommendation services, it is important to account for the interests of all content providers, encompassing not only newcomers but also minority demographic groups.…
FRCSyn Challenge at WACV 2024:Face Recognition Challenge in the Era of Synthetic Data
Pietro Melzi, Ruben Tolosana, Ruben Vera-Rodriguez +44
Despite the widespread adoption of face recognition technology around the world, and its remarkable performance on current benchmarks, there are still several challenges that must…
Faithful Path Language Modeling for Explainable Recommendation over Knowledge Graph
Giacomo Balloccu, Ludovico Boratto, Christian Cancedda +2
The integration of path reasoning with language modeling in recommender systems has shown promise for enhancing explainability but often struggles with the authenticity of the expl…
Counterfactual Graph Augmentation for Consumer Unfairness Mitigation in Recommender Systems
Ludovico Boratto, Francesco Fabbri, Gianni Fenu +2
In recommendation literature, explainability and fairness are becoming two prominent perspectives to consider. However, prior works have mostly addressed them separately, for insta…
(Un)fair Exposure in Deep Face Rankings at a Distance
Andrea Atzori, Gianni Fenu, Mirko Marras
Law enforcement regularly faces the challenge of ranking suspects from their facial images. Deep face models aid this process but frequently introduce biases that disproportionatel…
GNNUERS: Fairness Explanation in GNNs for Recommendation via Counterfactual Reasoning
Giacomo Medda, Francesco Fabbri, Mirko Marras +2
Nowadays, research into personalization has been focusing on explainability and fairness. Several approaches proposed in recent works are able to explain individual recommendations…
Knowledge is Power, Understanding is Impact: Utility and Beyond Goals, Explanation Quality, and Fairness in Path Reasoning Recommendation
Giacomo Balloccu, Ludovico Boratto, Christian Cancedda +2
Path reasoning is a notable recommendation approach that models high-order user-product relations, based on a Knowledge Graph (KG). This approach can extract reasoning paths betwee…
Protected Attributes Tell Us Who, Behavior Tells Us How: A Comparison of Demographic and Behavioral Oversampling for Fair Student Success Modeling
Jade Maï Cock, Muhammad Bilal, Richard Davis +2
Algorithms deployed in education can shape the learning experience and success of a student. It is therefore important to understand whether and how such algorithms might create in…
Do Not Trust a Model Because It is Confident: Uncovering and Characterizing Unknown Unknowns to Student Success Predictors in Online-Based Learning
Roberta Galici, Tanja Käser, Gianni Fenu +1
Student success models might be prone to develop weak spots, i.e., examples hard to accurately classify due to insufficient representation during model creation. This weakness is o…
Trusting the Explainers: Teacher Validation of Explainable Artificial Intelligence for Course Design
Vinitra Swamy, Sijia Du, Mirko Marras +1
Deep learning models for learning analytics have become increasingly popular over the last few years; however, these approaches are still not widely adopted in real-world settings,…
The More Secure, The Less Equally Usable: Gender and Ethnicity (Un)fairness of Deep Face Recognition along Security Thresholds
Andrea Atzori, Gianni Fenu, Mirko Marras
Face biometrics are playing a key role in making modern smart city applications more secure and usable. Commonly, the recognition threshold of a face recognition system is adjusted…
Reinforcement Recommendation Reasoning through Knowledge Graphs for Explanation Path Quality
Giacomo Balloccu, Ludovico Boratto, Gianni Fenu +1
Numerous Knowledge Graphs (KGs) are being created to make Recommender Systems (RSs) not only intelligent but also knowledgeable. Integrating a KG in the recommendation process allo…
Explaining Bias in Deep Face Recognition via Image Characteristics
Andrea Atzori, Gianni Fenu, Mirko Marras
In this paper, we propose a novel explanatory framework aimed to provide a better understanding of how face recognition models perform as the underlying data characteristics (prote…
Experts' View on Challenges and Needs for Fairness in Artificial Intelligence for Education
Gianni Fenu, Roberta Galici, Mirko Marras
In recent years, there has been a stimulating discussion on how artificial intelligence (AI) can support the science and engineering of intelligent educational applications. Many s…
Regulating Group Exposure for Item Providers in Recommendation
Mirko Marras, Ludovico Boratto, Guilherme Ramos +1
Engaging all content providers, including newcomers or minority demographic groups, is crucial for online platforms to keep growing and working. Hence, while building recommendatio…
Post Processing Recommender Systems with Knowledge Graphs for Recency, Popularity, and Diversity of Explanations
Giacomo Balloccu, Ludovico Boratto, Gianni Fenu +1
Existing explainable recommender systems have mainly modeled relationships between recommended and already experienced products, and shaped explanation types accordingly (e.g., mov…