22 citations · 77 across the 15 of their papers we have counts for
5 papers · 1 filter
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…
Learn Fast, Segment Well: Fast Object Segmentation Learning on the iCub Robot
Federico Ceola, Elisa Maiettini, Giulia Pasquale +3
The visual system of a robot has different requirements depending on the application: it may require high accuracy or reliability, be constrained by limited resources or need fast…
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…
Fast Object Segmentation Learning with Kernel-based Methods for Robotics
Federico Ceola, Elisa Maiettini, Giulia Pasquale +2
Object segmentation is a key component in the visual system of a robot that performs tasks like grasping and object manipulation, especially in presence of occlusions. Like many ot…
Fast Region Proposal Learning for Object Detection for Robotics
Federico Ceola, Elisa Maiettini, Giulia Pasquale +2
Object detection is a fundamental task for robots to operate in unstructured environments. Today, there are several deep learning algorithms that solve this task with remarkable pe…