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pro vyhledávání: '"Loghmani, Mohammad Reza"'
Open Set Domain Adaptation (OSDA) bridges the domain gap between a labeled source domain and an unlabeled target domain, while also rejecting target classes that are not present in the source. To avoid negative transfer, OSDA can be tackled by first
Externí odkaz:
http://arxiv.org/abs/2007.12360
Autor:
Loghmani, Mohammad Reza, Robbiano, Luca, Planamente, Mirco, Park, Kiru, Caputo, Barbara, Vincze, Markus
Unsupervised Domain Adaptation (DA) exploits the supervision of a label-rich source dataset to make predictions on an unlabeled target dataset by aligning the two data distributions. In robotics, DA is used to take advantage of automatically generate
Externí odkaz:
http://arxiv.org/abs/2004.10016
Typically a classifier trained on a given dataset (source domain) does not performs well if it is tested on data acquired in a different setting (target domain). This is the problem that domain adaptation (DA) tries to overcome and, while it is a wel
Externí odkaz:
http://arxiv.org/abs/1807.11697
Technological development aims to produce generations of increasingly efficient robots able to perform complex tasks. This requires considerable efforts, from the scientific community, to find new algorithms that solve computer vision problems, such
Externí odkaz:
http://arxiv.org/abs/1808.01357
Providing machines with the ability to recognize objects like humans has always been one of the primary goals of machine vision. The introduction of RGB-D cameras has paved the way for a significant leap forward in this direction thanks to the rich i
Externí odkaz:
http://arxiv.org/abs/1806.01673
The ability to recognize objects is an essential skill for a robotic system acting in human-populated environments. Despite decades of effort from the robotic and vision research communities, robots are still missing good visual perceptual systems, p
Externí odkaz:
http://arxiv.org/abs/1709.05862
Publikováno v:
In Pattern Recognition Letters August 2020 136:198-204
Autor:
Loghmani, Mohammad Reza
Object recognition, or object classification, is an essential skill for robot visual perception systems since it constitutes the foundation for higher-level tasks like object detection, pose estimation and manipulation. Nonetheless, recognizing objec
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::271b44b8842496585859ade7ad20720d
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