Zobrazeno 1 - 10
of 187
pro vyhledávání: '"Barbara Caputo"'
Publikováno v:
IEEE Access, Vol 12, Pp 57043-57058 (2024)
Federated Learning (FL) allows training machine learning models in privacy-constrained scenarios by enabling the cooperation of edge devices without requiring local data sharing. This approach raises several challenges due to the different statistica
Externí odkaz:
https://doaj.org/article/1be9762d70e546808673fc283b5ee9c5
Publikováno v:
IEEE Access, Vol 11, Pp 13324-13333 (2023)
We investigate the task of unsupervised domain adaptation in aerial semantic segmentation observing that there are some shortcomings in the class mixing strategies used by the recent state-of-the-art methods that tackle this task: 1) they do not acco
Externí odkaz:
https://doaj.org/article/a375411c875d4c208d283d2ba742476d
Publikováno v:
Frontiers in Computer Science, Vol 4 (2022)
We tackle the task of cross-domain visual geo-localization, where the goal is to geo-localize a given query image against a database of geo-tagged images, in the case where the query and the database belong to different visual domains. In particular,
Externí odkaz:
https://doaj.org/article/e218e7a6218147f2b3bbcb1672de427a
Autor:
Carlo Masone, Barbara Caputo
Publikováno v:
IEEE Access, Vol 9, Pp 19516-19547 (2021)
In recent years visual place recognition (VPR), i.e., the problem of recognizing the location of images, has received considerable attention from multiple research communities, spanning from computer vision to robotics and even machine learning. This
Externí odkaz:
https://doaj.org/article/de9f9554faf5435b99730fff558511b2
Autor:
Barbara Caputo, Luo Jie
Publikováno v:
ELCVIA Electronic Letters on Computer Vision and Image Analysis, Vol 8, Iss 3 (2010)
Local features have repeatedly shown their effectiveness for object recognition during the last years, and they have consequently become the preferred descriptor for this type of problems. The solution of the correspondence problem is traditionally a
Externí odkaz:
https://doaj.org/article/e2008c32c6764ed4954166a331f48e81
Autor:
Barbara Caputo
Publikováno v:
ELCVIA Electronic Letters on Computer Vision and Image Analysis, Vol 7, Iss 2 (2008)
Feature selection is crucial for effective object recognition. The subject has been vastly investigated in the literature, with approaches spanning from heuristic choices to statistical methods, to integration of multiple cues. For all these techniqu
Externí odkaz:
https://doaj.org/article/0a95372191ee4d8aa116556226bca441
Autor:
Barbara Caputo
Publikováno v:
American Journal of Islam and Society, Vol 21, Iss 2 (2004)
Through networks and media, European Muslims finally emerged as social and public actors in both European societies and the context of the broader ummah. This is the core subject of the book, an edited collection that examines the networks and ways i
Externí odkaz:
https://doaj.org/article/547b224cf8394c8e9eebfb21ee1c0a63
Publikováno v:
Human-Friendly Robotics 2022 ISBN: 9783031227301
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c954900f3fb247fecee37380ff9dc9f8
http://hdl.handle.net/11583/2971272
http://hdl.handle.net/11583/2971272
To enable a safe and effective human-robot cooperation, it is crucial to develop models for the identification of human activities. Egocentric vision seems to be a viable solution to solve this problem, and therefore many works provide deep learning
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::909e41f51872f35cf2bb0f81ab7228b4
http://arxiv.org/abs/2211.03004
http://arxiv.org/abs/2211.03004
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