Zobrazeno 1 - 10
of 13
pro vyhledávání: '"Marco Toldo"'
Publikováno v:
Technologies, Vol 8, Iss 2, p 35 (2020)
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 attracting a wide interest since semantic segmentation models require a huge
Externí odkaz:
https://doaj.org/article/48bc850e96d1412092754e5df94c7aa8
Publikováno v:
IEEE Internet of Things Journal. 10:1517-1535
Publikováno v:
The Visual Computer.
Deep learning models obtain impressive accuracy in road scenes understanding, however they need a large quantity of labeled samples for their training. Additionally, such models do not generalise well to environments where the statistical properties
Autor:
Donald Shenaj, Eros Fani, Marco Toldo, Debora Caldarola, Antonio Tavera, Umberto Michieli, Marco Ciccone, Pietro Zanuttigh, Barbara Caputo
Federated Learning (FL) has recently emerged as a possible way to tackle the domain shift in real-world Semantic Segmentation (SS) without compromising the private nature of the collected data. However, most of the existing works on FL unrealisticall
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::240c9fb3a5e11c162e45c77d7a4e113c
http://arxiv.org/abs/2210.02326
http://arxiv.org/abs/2210.02326
Autor:
Marco Toldo, Mete Ozay
Publikováno v:
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
Autor:
Sathyanarayanan Aakur, Yogesh Balaji, Han Cai, Zhaowei Cai, Andrea Cavallaro, Rama Chellappa, Dongdong Chen, E.R. Davies, Michael Felsberg, Cornelia Fermüller, Efstratios Gavves, Deepak Gupta, Song Han, Gang Hua, Ali Krayani, Ji Lin, Lucio Marcenaro, Michael Maynord, Umberto Michieli, Ramy Mounir, Hien Nguyen, Changjae Oh, Sujoy Paul, Carlo Regazzoni, Amit K. Roy-Chowdhury, Sudeep Sarkar, Giulia Slavic, Radu Timofte, Marco Toldo, Hassan Ugail, Nuno Vasconcelos, Alessio Xompero, Pietro Zanuttigh, Kai Zhang
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::28b5c5fb0b8c7730198650e9c2346553
https://doi.org/10.1016/b978-0-12-822109-9.00006-0
https://doi.org/10.1016/b978-0-12-822109-9.00006-0
Publikováno v:
2021 IEEE/CVF International Conference on Computer Vision (ICCV).
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 settings where new classes are learned in incremental steps and previous trai
Publikováno v:
ICPR
Unsupervised Domain Adaptation (UDA) aims at improving the generalization capability of a model trained on a source domain to perform well on a target domain for which no labeled data is available. In this paper, we consider the semantic segmentation
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::79670e0684d043a335306793bab1bff2
http://hdl.handle.net/11577/3390552
http://hdl.handle.net/11577/3390552
Publikováno v:
WACV
Deep learning frameworks allowed for a remarkable advancement in semantic segmentation, but the data hungry nature of convolutional networks has rapidly raised the demand for adaptation techniques able to transfer learned knowledge from label-abundan
Publikováno v:
CVPR Workshops
Deep convolutional neural networks for semantic segmentation achieve outstanding accuracy, however they also have a couple of major drawbacks: first, they do not generalize well to distributions slightly different from the one of the training data; s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d758eefe980eed01376a0c4b2bb24ac8