Zobrazeno 1 - 10
of 7 180
pro vyhledávání: '"A. Hermosilla"'
Traditionally, algorithms that learn to segment object instances in 2D images have heavily relied on large amounts of human-annotated data. Only recently, novel approaches have emerged tackling this problem in an unsupervised fashion. Generally, thes
Externí odkaz:
http://arxiv.org/abs/2411.16319
Autor:
Weijler, Lisa, Reiter, Michael, Hermosilla, Pedro, Maurer-Granofszky, Margarita, Dworzak, Michael
This paper evaluates various deep learning methods for measurable residual disease (MRD) detection in flow cytometry (FCM) data, addressing questions regarding the benefits of modeling long-range dependencies, methods of obtaining global information,
Externí odkaz:
http://arxiv.org/abs/2411.15621
In this paper, we study the Cauchy problem of the classical incompressible Navier--Stokes equations and the parabolic-elliptic Keller--Segel system in the framework of the Fourier--Besov spaces with variable regularity and integrability indices. By f
Externí odkaz:
http://arxiv.org/abs/2410.05293
In this paper, we are concerned with the well-posed issues of the fractional dissipative system in the framework of the Fourier--Besov spaces with variable regularity and integrability indices. By fully using some basic properties of these variable f
Externí odkaz:
http://arxiv.org/abs/2410.00060
We introduce G-Style, a novel algorithm designed to transfer the style of an image onto a 3D scene represented using Gaussian Splatting. Gaussian Splatting is a powerful 3D representation for novel view synthesis, as -- compared to other approaches b
Externí odkaz:
http://arxiv.org/abs/2408.15695
We consider a viscous compressible barotropic flow in the interval $[0,\pi]$ with homogeneous Dirichlet boundary conditions for the flow velocity and a constant rest state as initial data. Given two sufficiently close subintervals $I=[\alpha_1,\alpha
Externí odkaz:
http://arxiv.org/abs/2407.19210
In this paper, we are mainly concerned with the well-posed problem of the fractional Keller--Segel system in the framework of variable Lebesgue spaces. Based on carefully examining the algebraical structure of the system, we reduced the fractional Ke
Externí odkaz:
http://arxiv.org/abs/2405.01209
Autor:
Vergara-Hermosilla, Gastón
In this work we address some questions concerning the Cauchy problem for a generalized nonlinear heat equations considering as functional framework the variable Lebesgue spaces $L^{p(\cdot)}(\mathbb{R}^n)$. More precisely, by mixing some structural p
Externí odkaz:
http://arxiv.org/abs/2404.09588
In this paper, we are mainly concerned with the well-posedness of the dissipative surface quasi-geostrophic equation in the framework of variable Lebesgue spaces. Based on some analytical results developed in the variable Lebesgue spaces and the $L^{
Externí odkaz:
http://arxiv.org/abs/2404.05127
Test-Time Training (TTT) proposes to adapt a pre-trained network to changing data distributions on-the-fly. In this work, we propose the first TTT method for 3D semantic segmentation, TTT-KD, which models Knowledge Distillation (KD) from foundation m
Externí odkaz:
http://arxiv.org/abs/2403.11691