Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Enrico Meloni"'
Publikováno v:
Continual Semi-Supervised Learning ISBN: 9783031175862
Continual learning refers to the ability of humans and animals to incrementally learn over time in a given environment. Trying to simulate this learning process in machines is a challenging task, also due to the inherent difficulty in creating condit
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0d0a2a97076194a1844347db39ccbd46
https://hdl.handle.net/11365/1206736
https://hdl.handle.net/11365/1206736
Publikováno v:
ICPR
ICPR 2020-25th International Conference on Pattern Recognition
ICPR 2020-25th International Conference on Pattern Recognition, Jan 2021, Milan / Virtual, Italy
ICPR 2020-25th International Conference on Pattern Recognition, Jan 2021, Milan / Virtual, Italy. ⟨10.1109/ICPR48806.2021.9412909⟩
ICPR 2020-25th International Conference on Pattern Recognition
ICPR 2020-25th International Conference on Pattern Recognition, Jan 2021, Milan / Virtual, Italy
ICPR 2020-25th International Conference on Pattern Recognition, Jan 2021, Milan / Virtual, Italy. ⟨10.1109/ICPR48806.2021.9412909⟩
Recently, researchers in Machine Learning algorithms, Computer Vision scientists, engineers and others, showed a growing interest in 3D simulators as a mean to artificially create experimental settings that are very close to those in the real world.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c1e4ba0e886a9d7a5728476b885349c9
https://doi.org/10.1109/icpr48806.2021.9412909
https://doi.org/10.1109/icpr48806.2021.9412909
In the last few years, the scientific community showed a remarkable and increasing interest towards 3D Virtual Environments, training and testing Machine Learning-based models in realistic virtual worlds. On one hand, these environments could also be
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2cd30fd17f33034a53aca7bc2febf52d
Autor:
Giuseppe Amato, Fabrizio Falchi, Enrico Meloni, Claudio Gennaro, Marco Di Benedetto, Fabio Carrara
Publikováno v:
Multimedia tools and applications (2020). doi:10.1007/s11042-020-09597-9
info:cnr-pdr/source/autori:Di Benedetto M.; Carrara F.; Meloni E.; Amato G.; Falchi F.; Gennaro C./titolo:Learning accurate personal protective equipment detection from virtual worlds/doi:10.1007%2Fs11042-020-09597-9/rivista:Multimedia tools and applications/anno:2020/pagina_da:/pagina_a:/intervallo_pagine:/volume
info:cnr-pdr/source/autori:Di Benedetto M.; Carrara F.; Meloni E.; Amato G.; Falchi F.; Gennaro C./titolo:Learning accurate personal protective equipment detection from virtual worlds/doi:10.1007%2Fs11042-020-09597-9/rivista:Multimedia tools and applications/anno:2020/pagina_da:/pagina_a:/intervallo_pagine:/volume
Deep learning has achieved impressive results in many machine learning tasks such as image recognition and computer vision. Its applicability to supervised problems is however constrained by the availability of high-quality training data consisting o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e1a06149a91bc32ffa488a558e1cba14
http://www.cnr.it/prodotto/i/438782
http://www.cnr.it/prodotto/i/438782
Publikováno v:
2019 International Conference on Content-Based Multimedia Indexing (CBMI), Dublin, Ireland, 4/9/2019, 6/9/2019
info:cnr-pdr/source/autori:Di Benedetto M.; Meloni E.; Amato G.; Falchi F.; Gennaro C./congresso_nome:2019 International Conference on Content-Based Multimedia Indexing (CBMI)/congresso_luogo:Dublin, Ireland/congresso_data:4%2F9%2F2019, 6%2F9%2F2019/anno:2019/pagina_da:/pagina_a:/intervallo_pagine
CBMI
2019 International Conference on Content-Based Multimedia Indexing (CBMI)
info:cnr-pdr/source/autori:Di Benedetto M.; Meloni E.; Amato G.; Falchi F.; Gennaro C./congresso_nome:2019 International Conference on Content-Based Multimedia Indexing (CBMI)/congresso_luogo:Dublin, Ireland/congresso_data:4%2F9%2F2019, 6%2F9%2F2019/anno:2019/pagina_da:/pagina_a:/intervallo_pagine
CBMI
2019 International Conference on Content-Based Multimedia Indexing (CBMI)
Nowadays, the possibilities offered by state-of-The-Art deep neural networks allow the creation of systems capable of recognizing and indexing visual content with very high accuracy. Performance of these systems relies on the availability of high qua
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7c3742242a5f3697b73f909d8328bc13
http://www.cnr.it/prodotto/i/411370
http://www.cnr.it/prodotto/i/411370