28 citations · 28 across the 7 of their papers we have counts for
7 papers
Trust And Balance: Few Trusted Samples Pseudo-Labeling and Temperature Scaled Loss for Effective Source-Free Unsupervised Domain Adaptation
Andrea Maracani, Lorenzo Rosasco, Lorenzo Natale
Deep Neural Networks have significantly impacted many computer vision tasks. However, their effectiveness diminishes when test data distribution (target domain) deviates from the o…
Key Design Choices in Source-Free Unsupervised Domain Adaptation: An In-depth Empirical Analysis
Andrea Maracani, Raffaello Camoriano, Elisa Maiettini +3
This study provides a comprehensive benchmark framework for Source-Free Unsupervised Domain Adaptation (SF-UDA) in image classification, aiming to achieve a rigorous empirical unde…
Sim2Real Bilevel Adaptation for Object Surface Classification using Vision-Based Tactile Sensors
Gabriele M. Caddeo, Andrea Maracani, Paolo D. Alfano +3
In this paper, we address the Sim2Real gap in the field of vision-based tactile sensors for classifying object surfaces. We train a Diffusion Model to bridge this gap using a relat…
Key Design Choices for Double-Transfer in Source-Free Unsupervised Domain Adaptation
Andrea Maracani, Raffaello Camoriano, Elisa Maiettini +3
Fine-tuning and Domain Adaptation emerged as effective strategies for efficiently transferring deep learning models to new target tasks. However, target domain labels are not acces…
RECALL: Replay-based Continual Learning in Semantic Segmentation
Andrea Maracani, Umberto Michieli, Marco Toldo +1
Deep networks allow to obtain outstanding results in semantic segmentation, however they need to be trained in a single shot with a large amount of data. Continual learning setting…
From Handheld to Unconstrained Object Detection: a Weakly-supervised On-line Learning Approach
Elisa Maiettini, Andrea Maracani, Raffaello Camoriano +4
Deep Learning (DL) based methods for object detection achieve remarkable performance at the cost of computationally expensive training and extensive data labeling. Robots embodimen…
Unsupervised Domain Adaptation in Semantic Segmentation: a Review
Marco Toldo, Andrea Maracani, Umberto Michieli +1
The aim of this paper is to give an overview of the recent advancements in the Unsupervised Domain Adaptation (UDA) of deep networks for semantic segmentation. This task is attract…