Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Ziko, Imtiaz Masud"'
We introduce a simple non-linear embedding adaptation layer, which is fine-tuned on top of fixed pre-trained features for one-shot tasks, improving significantly transductive entropy-based inference for low-shot regimes. Our norm-induced transformati
Externí odkaz:
http://arxiv.org/abs/2304.06832
We investigate a general formulation for clustering and transductive few-shot learning, which integrates prototype-based objectives, Laplacian regularization and supervision constraints from a few labeled data points. We propose a concave-convex rela
Externí odkaz:
http://arxiv.org/abs/2106.09516
We propose a transductive Laplacian-regularized inference for few-shot tasks. Given any feature embedding learned from the base classes, we minimize a quadratic binary-assignment function containing two terms: (1) a unary term assigning query samples
Externí odkaz:
http://arxiv.org/abs/2006.15486
Autor:
Boudiaf, Malik, Rony, Jérôme, Ziko, Imtiaz Masud, Granger, Eric, Pedersoli, Marco, Piantanida, Pablo, Ayed, Ismail Ben
Recently, substantial research efforts in Deep Metric Learning (DML) focused on designing complex pairwise-distance losses, which require convoluted schemes to ease optimization, such as sample mining or pair weighting. The standard cross-entropy los
Externí odkaz:
http://arxiv.org/abs/2003.08983
We propose a general variational framework of fair clustering, which integrates an original Kullback-Leibler (KL) fairness term with a large class of clustering objectives, including prototype or graph based. Fundamentally different from the existing
Externí odkaz:
http://arxiv.org/abs/1906.08207
We advocate Laplacian K-modes for joint clustering and density mode finding, and propose a concave-convex relaxation of the problem, which yields a parallel algorithm that scales up to large datasets and high dimensions. We optimize a tight bound (au
Externí odkaz:
http://arxiv.org/abs/1810.13044
Autor:
Ismail Ben Ayed, Malik Boudiaf, Ziko Imtiaz Masud, Hoel Kervadec, Pablo Piantanida, Jose Dolz
Publikováno v:
CVPR
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
We show that the way inference is performed in few-shot segmentation tasks has a substantial effect on performances -- an aspect often overlooked in the literature in favor of the meta-learning paradigm. We introduce a transductive inference for a gi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::eee851c248afb91657d14820dea78d69
https://doi.org/10.1109/cvpr46437.2021.01376
https://doi.org/10.1109/cvpr46437.2021.01376
Publikováno v:
2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR); 2015, p356-360, 5p
Publikováno v:
Image & Signal Processing (9783319079974); 2014, p531-538, 8p