5 citations · 7 across the 9 of their papers we have counts for
15 papers
Fairness meets Cross-Domain Learning: a new perspective on Models and Metrics
Leonardo Iurada, Silvia Bucci, Timothy M. Hospedales +1
Deep learning-based recognition systems are deployed at scale for several real-world applications that inevitably involve our social life. Although being of great support when maki…
WiCV 2022: The Tenth Women In Computer Vision Workshop
Doris Antensteiner, Silvia Bucci, Arushi Goel +6
In this paper, we present the details of Women in Computer Vision Workshop - WiCV 2022, organized alongside the hybrid CVPR 2022 in New Orleans, Louisiana. It provides a voice to a…
Semantic Novelty Detection via Relational Reasoning
Francesco Cappio Borlino, Silvia Bucci, Tatiana Tommasi
Semantic novelty detection aims at discovering unknown categories in the test data. This task is particularly relevant in safety-critical applications, such as autonomous driving o…
Contrastive Learning for Cross-Domain Open World Recognition
Francesco Cappio Borlino, Silvia Bucci, Tatiana Tommasi
The ability to evolve is fundamental for any valuable autonomous agent whose knowledge cannot remain limited to that injected by the manufacturer. Consider for example a home assis…
Distance-based Hyperspherical Classification for Multi-source Open-Set Domain Adaptation
Silvia Bucci, Francesco Cappio Borlino, Barbara Caputo +1
Vision systems trained in closed-world scenarios fail when presented with new environmental conditions, new data distributions, and novel classes at deployment time. How to move to…
Towards Fairness Certification in Artificial Intelligence
Tatiana Tommasi, Silvia Bucci, Barbara Caputo +1
Thanks to the great progress of machine learning in the last years, several Artificial Intelligence (AI) techniques have been increasingly moving from the controlled research labor…
Self-Supervision & Meta-Learning for One-Shot Unsupervised Cross-Domain Detection
F. Cappio Borlino, S. Polizzotto, B. Caputo +1
Deep detection approaches are powerful in controlled conditions, but appear brittle and fail when source models are used off-the-shelf on unseen domains. Most of the existing works…
Multi-Modal RGB-D Scene Recognition Across Domains
Andrea Ferreri, Silvia Bucci, Tatiana Tommasi
Scene recognition is one of the basic problems in computer vision research with extensive applications in robotics. When available, depth images provide helpful geometric cues that…
On the Effectiveness of Image Rotation for Open Set Domain Adaptation
Silvia Bucci, Mohammad Reza Loghmani, Tatiana Tommasi
Open Set Domain Adaptation (OSDA) bridges the domain gap between a labeled source domain and an unlabeled target domain, while also rejecting target classes that are not present in…
Self-Supervised Learning Across Domains
Silvia Bucci, Antonio D'Innocente, Yujun Liao +3
Human adaptability relies crucially on learning and merging knowledge from both supervised and unsupervised tasks: the parents point out few important concepts, but then the childr…
One-Shot Unsupervised Cross-Domain Detection
Antonio D'Innocente, Francesco Cappio Borlino, Silvia Bucci +2
Despite impressive progress in object detection over the last years, it is still an open challenge to reliably detect objects across visual domains. Although the topic has attracte…
Learning to Generalize One Sample at a Time with Self-Supervision
Antonio D'Innocente, Silvia Bucci, Barbara Caputo +1
Although deep networks have significantly increased the performance of visual recognition methods, it is still challenging to achieve the robustness across visual domains that is n…
Tackling Partial Domain Adaptation with Self-Supervision
Silvia Bucci, Antonio D'Innocente, Tatiana Tommasi
Domain adaptation approaches have shown promising results in reducing the marginal distribution difference among visual domains. They allow to train reliable models that work over…
Domain Generalization by Solving Jigsaw Puzzles
Fabio Maria Carlucci, Antonio D'Innocente, Silvia Bucci +2
Human adaptability relies crucially on the ability to learn and merge knowledge both from supervised and unsupervised learning: the parents point out few important concepts, but th…
Multimodal Deep Domain Adaptation
Silvia Bucci, Mohammad Reza Loghmani, Barbara Caputo
Typically a classifier trained on a given dataset (source domain) does not performs well if it is tested on data acquired in a different setting (target domain). This is the proble…