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of 45
pro vyhledávání: '"Jiu, Mingyuan"'
Few-shot detection is a major task in pattern recognition which seeks to localize objects using models trained with few labeled data. One of the mainstream few-shot methods is transfer learning which consists in pretraining a detection model in a sou
Externí odkaz:
http://arxiv.org/abs/2402.09315
Autor:
Jiu, Mingyuan, Pustelnik, Nelly
Publikováno v:
IEEE Signal Processing Letters 2022
This work designs an image restoration deep network relying on unfolded Chambolle-Pock primal-dual iterations. Each layer of our network is built from Chambolle-Pock iterations when specified for minimizing a sum of a $\ell_2$-norm data-term and an a
Externí odkaz:
http://arxiv.org/abs/2202.09810
Autor:
Jiu, Mingyuan, Sahbi, Hichem
Context modeling is one of the most fertile subfields of visual recognition which aims at designing discriminant image representations while incorporating their intrinsic and extrinsic relationships. However, the potential of context modeling is curr
Externí odkaz:
http://arxiv.org/abs/2012.11253
Autor:
Jiu, Mingyuan, Pustelnik, Nelly
Image restoration remains a challenging task in image processing. Numerous methods tackle this problem, often solved by minimizing a non-smooth penalized co-log-likelihood function. Although the solution is easily interpretable with theoretic guarant
Externí odkaz:
http://arxiv.org/abs/2007.00959
Autor:
Jiu, Mingyuan, Sahbi, Hichem
Deep kernel map networks have shown excellent performances in various classification problems including image annotation. Their general recipe consists in aggregating several layers of singular value decompositions (SVDs) -- that map data from input
Externí odkaz:
http://arxiv.org/abs/2006.15088
Autor:
Jiu, Mingyuan, Sahbi, Hichem
Context plays a crucial role in visual recognition as it provides complementary clues for different learning tasks including image classification and annotation. As the performances of these tasks are currently reaching a plateau, any extra knowledge
Externí odkaz:
http://arxiv.org/abs/1912.12735
Autor:
Jiu, Mingyuan, Sahbi, Hichem
Deep kernel learning aims at designing nonlinear combinations of multiple standard elementary kernels by training deep networks. This scheme has proven to be effective, but intractable when handling large-scale datasets especially when the depth of t
Externí odkaz:
http://arxiv.org/abs/1804.11159
Autor:
Jiu, Mingyuan, Sahbi, Hichem
Context plays an important role in visual pattern recognition as it provides complementary clues for different learning tasks including image classification and annotation. In the particular scenario of kernel learning, the general recipe of context-
Externí odkaz:
http://arxiv.org/abs/1803.08794
Autor:
Jiu, Mingyuan, Sahbi, Hichem
Publikováno v:
In Neurocomputing 14 February 2022 474:154-167
This work focuses on learning optimization problems with quadratical interactions between variables, which go beyond the additive models of traditional linear learning. We investigate more specifically two different methods encountered in the literat
Externí odkaz:
http://arxiv.org/abs/1705.07817