Zobrazeno 1 - 10
of 15
pro vyhledávání: '"Antonio Tavera"'
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
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:
Sepideh Norouzi, Antonio Tavera-Vazquez, Johanan Ramirez-de Arellano, Dae Seok Kim, Teresa Lopez-Leon, Juan J. de Pablo, Jose A. Martinez-Gonzalez, Monirosadat Sadati
Publikováno v:
ACS nano. 16(10)
Many crystallization processes, including biomineralization and ice-freezing, occur in small and curved volumes, where surface curvature can strain the crystal, leading to unusual configurations and defect formation. The role of curvature on crystall
Publikováno v:
Image Analysis and Processing – ICIAP 2022 ISBN: 9783031064296
Incremental learning represents a crucial task in aerial image processing, especially given the limited availability of large-scale annotated datasets. A major issue concerning current deep neural architectures is known as catastrophic forgetting, na
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9c72924a638e058a77c2c6b45eb184fc
https://doi.org/10.1007/978-3-031-06430-2_62
https://doi.org/10.1007/978-3-031-06430-2_62
Publikováno v:
2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV).
In this paper we consider the task of semantic segmentation in autonomous driving applications. Specifically, we consider the cross-domain few-shot setting where training can use only few real-world annotated images and many annotated synthetic image
Autor:
Lidia Fantauzzo, Eros Fani, Debora Caldarola, Antonio Tavera, Fabio Cermelli, Marco Ciccone, Barbara Caputo
Semantic Segmentation is essential to make self-driving vehicles autonomous, enabling them to understand their surroundings by assigning individual pixels to known categories. However, it operates on sensible data collected from the users' cars; thus
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5b3da7166d0a2b44ce3b50b53263015b
http://hdl.handle.net/11583/2970566
http://hdl.handle.net/11583/2970566
Augmentation Invariance and Adaptive Sampling in Semantic Segmentation of Agricultural Aerial Images
In this paper, we investigate the problem of Semantic Segmentation for agricultural aerial imagery. We observe that the existing methods used for this task are designed without considering two characteristics of the aerial data: (i) the top-down pers
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7a4f24f171aa2a0b199f035de165fbe4
Publikováno v:
Image Analysis and Processing – ICIAP 2022 ISBN: 9783031064296
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::686f5c0ca6accc4a691e3d8aee9da975
https://doi.org/10.1007/978-3-031-06430-2_38
https://doi.org/10.1007/978-3-031-06430-2_38
Although existing semantic segmentation approaches achieve impressive results, they still struggle to update their models incrementally as new categories are uncovered. Furthermore, pixel-by-pixel annotations are expensive and time-consuming. This pa
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a16dac2f89c37d602d45fd31def4cd3e
http://arxiv.org/abs/2112.01882
http://arxiv.org/abs/2112.01882
Publikováno v:
IEEE Robotics and Automation Letters
Semantic segmentation is key in autonomous driving. Using deep visual learning architectures is not trivial in this context, because of the challenges in creating suitable large scale annotated datasets. This issue has been traditionally circumvented
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::236297b9590cb78e631a5b5bec30644f