5 citations · 5 across the 3 of their papers we have counts for
9 papers
Exploring Data Aggregation and Transformations to Generalize across Visual Domains
Antono D'Innocente
Computer vision has flourished in recent years thanks to Deep Learning advancements, fast and scalable hardware solutions and large availability of structured image data. Convoluti…
Rethinking Domain Generalization Baselines
Francesco Cappio Borlino, Antonio D'Innocente, Tatiana Tommasi
Despite being very powerful in standard learning settings, deep learning models can be extremely brittle when deployed in scenarios different from those on which they were trained.…
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…