no code implementations • 5 Nov 2021 • Roy Henha Eyono, Fabio Maria Carlucci, Pedro M Esperança, Binxin Ru, Phillip Torr
State-of-the-art results in deep learning have been improving steadily, in good part due to the use of larger models.
no code implementations • NeurIPS 2021 • Chen Zhang, Shifeng Zhang, Fabio Maria Carlucci, Zhenguo Li
To eliminate the requirement of saving separate models for different target datasets, we propose a novel setting that starts from a pretrained deep generative model and compresses the data batches while adapting the model with a dynamical system for only one epoch.
1 code implementation • 12 Feb 2021 • Luca Robbiano, Muhammad Rameez Ur Rahman, Fabio Galasso, Barbara Caputo, Fabio Maria Carlucci
Unsupervised Domain Adaptation (UDA) is a key issue in visual recognition, as it allows to bridge different visual domains enabling robust performances in the real world.
no code implementations • 1 Jan 2021 • Roy Henha Eyono, Fabio Maria Carlucci, Pedro M Esperança, Binxin Ru, Philip Torr
State-of-the-art results in deep learning have been improving steadily, in good part due to the use of larger models.
no code implementations • 24 Jul 2020 • Silvia Bucci, Antonio D'Innocente, Yujun Liao, Fabio Maria Carlucci, Barbara Caputo, Tatiana Tommasi
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 children fill in the gaps on their own.
Ranked #79 on Domain Generalization on PACS
no code implementations • 3 Sep 2019 • Vasco Lopes, Fabio Maria Carlucci, Pedro M Esperança, Marco Singh, Victor Gabillon, Antoine Yang, Hang Xu, Zewei Chen, Jun Wang
The Neural Architecture Search (NAS) problem is typically formulated as a graph search problem where the goal is to learn the optimal operations over edges in order to maximise a graph-level global objective.
2 code implementations • 16 Mar 2019 • Fabio Maria Carlucci, Antonio D'Innocente, Silvia Bucci, Barbara Caputo, Tatiana Tommasi
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 then the children fill in the gaps on their own.
Ranked #3 on Domain Generalization on NICO Animal
no code implementations • 13 Feb 2019 • Fabio Maria Carlucci
This thesis will focus on a family of transfer learning methods applied to the task of visual object recognition, specifically image classification.
no code implementations • CVPR 2018 • Paolo Russo, Fabio Maria Carlucci, Tatiana Tommasi, Barbara Caputo
The effectiveness of generative adversarial approaches in producing images according to a specific style or visual domain has recently opened new directions to solve the unsupervised domain adaptation problem.
Ranked #13 on Domain Adaptation on SVHN-to-MNIST
no code implementations • 5 May 2017 • Antonio D'Innocente, Fabio Maria Carlucci, Mirco Colosi, Barbara Caputo
Despite the impressive progress brought by deep network in visual object recognition, robot vision is still far from being a solved problem.
2 code implementations • ICCV 2017 • Fabio Maria Carlucci, Lorenzo Porzi, Barbara Caputo, Elisa Ricci, Samuel Rota Bulò
Here we take a different route, proposing to align the learned representations by embedding in any given network specific Domain Alignment Layers, designed to match the source and target feature distributions to a reference one.
no code implementations • 21 Feb 2017 • Fabio Maria Carlucci, Lorenzo Porzi, Barbara Caputo, Elisa Ricci, Samuel Rota Bulò
The empirical fact that classifiers, trained on given data collections, perform poorly when tested on data acquired in different settings is theoretically explained in domain adaptation through a shift among distributions of the source and target domains.
no code implementations • 30 Sep 2016 • Fabio Maria Carlucci, Paolo Russo, Barbara Caputo
We show that the filters learned from such data collection, using the very same architecture typically used on visual data, learns very different filters, resulting in depth features (a) able to better characterize the different facets of depth images, and (b) complementary with respect to those derived from CNNs pre-trained on 2D datasets.
no code implementations • CVPR 2016 • Ilja Kuzborskij, Fabio Maria Carlucci, Barbara Caputo
Since Convolutional Neural Networks (CNNs) have become the leading learning paradigm in visual recognition, Naive Bayes Nearest Neighbor (NBNN)-based classifiers have lost momentum in the community.
no code implementations • 12 Nov 2015 • Ilja Kuzborskij, Fabio Maria Carlucci, Barbara Caputo
Since Convolutional Neural Networks (CNNs) have become the leading learning paradigm in visual recognition, Naive Bayes Nearest Neighbour (NBNN)-based classifiers have lost momentum in the community.