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of 9
pro vyhledávání: '"Maxim Berman"'
As natural images usually contain multiple objects, multi-label image classification is more applicable "in the wild" than single-label classification. However, exhaustively annotating images with every object of interest is costly and time-consuming
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::15506db1ade5291ea3de816ecd9c6c1f
https://lirias.kuleuven.be/handle/20.500.12942/715919
https://lirias.kuleuven.be/handle/20.500.12942/715919
Autor:
Matthew B. Blaschko, Maxim Berman
Publikováno v:
International Journal of Computer Vision. 128:1722-1735
Problems of segmentation, denoising, registration and 3D reconstruction are often addressed with the graph cut algorithm. However, solving an unconstrained graph cut problem is NP-hard. For tractable optimization, pairwise potentials have to fulfill
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f595e8b2424c7c19afa3de08d572ff46
https://doi.org/10.1007/s11263-019-01277-y
https://doi.org/10.1007/s11263-019-01277-y
Autor:
Frederik Maes, Jeroen Bertels, Maxim Berman, Raf Bisschops, Matthew B. Blaschko, Tom Eelbode, Dirk Vandermeulen
In many medical imaging and classical computer vision tasks, the Dice score and Jaccard index are used to evaluate the segmentation performance. Despite the existence and great empirical success of metric-sensitive losses, i.e. relaxations of these m
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b912d9ba9189cc778fcb6214dca528e3
https://lirias.kuleuven.be/handle/123456789/665416
https://lirias.kuleuven.be/handle/123456789/665416
Publikováno v:
CVPR
Neural architecture search (NAS) approaches aim at automatically finding novel CNN architectures that fit computational constraints while maintaining a good performance on the target platform. We introduce a novel efficient one-shot NAS approach to o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e15ddcc152d7135354c5b7142a386442
https://lirias.kuleuven.be/handle/123456789/652121
https://lirias.kuleuven.be/handle/123456789/652121
Publikováno v:
ICCV Workshops
Deep neural networks (DNNs) have become increasingly important due to their excellent empirical performance on a wide range of problems. However, regularization is generally achieved by indirect means, largely due to the complex set of functions defi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::73975e6d828456c43031ba1835ddb1da
https://doi.org/10.1109/iccvw.2019.00097
https://doi.org/10.1109/iccvw.2019.00097
Autor:
Jacques Cali, Maxim Berman, Xingchen Ma, Christos Sagonas, Amal Rannen Triki, Matthew B. Blaschko
Publikováno v:
ICCV
Neural network compression is an important step for deploying neural networks where speed is of high importance, or on devices with limited memory. It is necessary to tune compression parameters in order to achieve the desired trade-off between size
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2b4f34f4f4af18db8ed58291d5ed822d
https://doi.org/10.1109/iccv.2019.01037
https://doi.org/10.1109/iccv.2019.01037
Publikováno v:
CVPR
The Jaccard index, also referred to as the intersection-over-union score, is commonly employed in the evaluation of image segmentation results given its perceptual qualities, scale invariance - which lends appropriate relevance to small objects, and
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7f56b06967e3392a9f726603244ae210
https://lirias.kuleuven.be/handle/123456789/621350
https://lirias.kuleuven.be/handle/123456789/621350
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783319494081
ECCV Workshops (3)
Geometry Meets Deep Learning Workshop in association with ECCV 2016
Geometry Meets Deep Learning Workshop in association with ECCV 2016, Oct 2016, Amsterdam, Netherlands. pp.296-308, ⟨10.1007/978-3-319-49409-8_24⟩
ECCV Workshops (3)
Geometry Meets Deep Learning Workshop in association with ECCV 2016
Geometry Meets Deep Learning Workshop in association with ECCV 2016, Oct 2016, Amsterdam, Netherlands. pp.296-308, ⟨10.1007/978-3-319-49409-8_24⟩
International audience; Our goal in this work is to recover an estimate of an object's surface from a single image. We address this severely ill-posed problem by employing a discriminatively-trained graphical model: we incorporate prior information a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7dc3864b388839918d8ddb8446e4ea81
https://doi.org/10.1007/978-3-319-49409-8_24
https://doi.org/10.1007/978-3-319-49409-8_24
Autor:
Matthew B. Blaschko, Dirk Vandermeulen, Maxim Berman, Frederik Maes, Tom Eelbode, Raf Bisschops, Jeroen Bertels
Publikováno v:
Lecture Notes in Computer Science
Lecture Notes in Computer Science-Medical Image Computing and Computer Assisted Intervention – MICCAI 2019
Medical Image Computing and Computer Assisted Intervention
Lecture Notes in Computer Science ISBN: 9783030322441
Lecture Notes in Computer Science-Medical Image Computing and Computer Assisted Intervention – MICCAI 2019
Medical Image Computing and Computer Assisted Intervention
Lecture Notes in Computer Science ISBN: 9783030322441
The Dice score and Jaccard index are commonly used metrics for the evaluation of segmentation tasks in medical imaging. Convolutional neural networks trained for image segmentation tasks are usually optimized for (weighted) cross-entropy. This introd
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::14c096a874a18a951d364d35afcabed6