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pro vyhledávání: '"Groenendijk, Rick"'
Signals from different modalities each have their own combination algebra which affects their sampling processing. RGB is mostly linear; depth is a geometric signal following the operations of mathematical morphology. If a network obtaining RGB-D inp
Externí odkaz:
http://arxiv.org/abs/2310.07669
Pooling is essentially an operation from the field of Mathematical Morphology, with max pooling as a limited special case. The more general setting of MorphPooling greatly extends the tool set for building neural networks. In addition to pooling oper
Externí odkaz:
http://arxiv.org/abs/2211.14037
Many interesting tasks in machine learning and computer vision are learned by optimising an objective function defined as a weighted linear combination of multiple losses. The final performance is sensitive to choosing the correct (relative) weights
Externí odkaz:
http://arxiv.org/abs/2009.01717
In this paper we address the benefit of adding adversarial training to the task of monocular depth estimation. A model can be trained in a self-supervised setting on stereo pairs of images, where depth (disparities) are an intermediate result in a ri
Externí odkaz:
http://arxiv.org/abs/1910.13340
Publikováno v:
In Computer Vision and Image Understanding January 2020 190
Akademický článek
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Akademický článek
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Autor:
Heinerman, Jacqueline, Bussmann, Bart, Groenendijk, Rick, Krieken, Emile Van, Slik, Jesper, Tezza, Alessandro, Haasdijk, Evert, Eiben, A. E., Sundaram, Suresh
Publikováno v:
2018 IEEE Symposium Series on Computational Intelligence (SSCI): [Proceedings], 851-858
STARTPAGE=851;ENDPAGE=858;TITLE=2018 IEEE Symposium Series on Computational Intelligence (SSCI)
Heinerman, J, Bussmann, B, Groenendijk, R, Krieken, E V, Slik, J, Tezza, A, Haasdijk, E & Eiben, A E 2019, Benefits of Social Learning in Physical Robots . in S Sundaram (ed.), 2018 IEEE Symposium Series on Computational Intelligence (SSCI) : [Proceedings] ., 8628857, Institute of Electrical and Electronics Engineers Inc., pp. 851-858, 8th IEEE Symposium Series on Computational Intelligence, SSCI 2018, Bangalore, India, 18/11/18 . https://doi.org/10.1109/SSCI.2018.8628857
SSCI
STARTPAGE=851;ENDPAGE=858;TITLE=2018 IEEE Symposium Series on Computational Intelligence (SSCI)
Heinerman, J, Bussmann, B, Groenendijk, R, Krieken, E V, Slik, J, Tezza, A, Haasdijk, E & Eiben, A E 2019, Benefits of Social Learning in Physical Robots . in S Sundaram (ed.), 2018 IEEE Symposium Series on Computational Intelligence (SSCI) : [Proceedings] ., 8628857, Institute of Electrical and Electronics Engineers Inc., pp. 851-858, 8th IEEE Symposium Series on Computational Intelligence, SSCI 2018, Bangalore, India, 18/11/18 . https://doi.org/10.1109/SSCI.2018.8628857
SSCI
Robot-to-robot learning, a specific case of social learning in robotics, enables the ability to transfer robot controllers directly from one robot to another. Previous studies showed that the exchange of controller information can increase learning s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0638cdad876231a7099c346bd82e3452
https://doi.org/10.1109/SSCI.2018.8628857
https://doi.org/10.1109/SSCI.2018.8628857
Publikováno v:
Climatic Change; Feb2024, Vol. 177 Issue 2, p1-1, 1p