Zobrazeno 1 - 10
of 7 518
pro vyhledávání: '"A. Charette"'
In this paper, we first propose a novel method for transferring material transformations across different scenes. Building on disentangled Neural Radiance Field (NeRF) representations, our approach learns to map Bidirectional Reflectance Distribution
Externí odkaz:
http://arxiv.org/abs/2411.08037
Domain adaptation has been extensively investigated in computer vision but still requires access to target data at the training time, which might be difficult to obtain in some uncommon conditions. In this paper, we present a new framework for domain
Externí odkaz:
http://arxiv.org/abs/2410.21361
Large-scale vision-language pre-trained (VLP) models (e.g., CLIP) are renowned for their versatility, as they can be applied to diverse applications in a zero-shot setup. However, when these models are used in specific domains, their performance ofte
Externí odkaz:
http://arxiv.org/abs/2410.08211
We consider the problem of adapting a contrastively pretrained vision-language model like CLIP (Radford et al., 2021) for few-shot classification. The literature addresses this problem by learning a linear classifier of the frozen visual features, op
Externí odkaz:
http://arxiv.org/abs/2410.05270
Autor:
Ghafourian, Amin, CuiZhu, Zhongying, Shi, Debo, Chuang, Ian, Charette, Francois, Sachdeva, Rithik, Soltani, Iman
Publikováno v:
in IEEE Robotics and Automation Letters, vol. 9, no. 4, pp. 3211-3218, April 2024
Human navigation is facilitated through the association of actions with landmarks, tapping into our ability to recognize salient features in our environment. Consequently, navigational instructions for humans can be extremely concise, such as short v
Externí odkaz:
http://arxiv.org/abs/2409.14633
We address prevailing challenges of the brain-powered research, departing from the observation that the literature hardly recover accurate spatial information and require subject-specific models. To address these challenges, we propose UMBRAE, a unif
Externí odkaz:
http://arxiv.org/abs/2404.07202
We propose the task of Panoptic Scene Completion (PSC) which extends the recently popular Semantic Scene Completion (SSC) task with instance-level information to produce a richer understanding of the 3D scene. Our PSC proposal utilizes a hybrid mask-
Externí odkaz:
http://arxiv.org/abs/2312.02158
Generalization to new domains not seen during training is one of the long-standing challenges in deploying neural networks in real-world applications. Existing generalization techniques either necessitate external images for augmentation, and/or aim
Externí odkaz:
http://arxiv.org/abs/2311.17922
Autor:
Lyubomyr Bohuta, MD, PhD, Titus Chan, MD, MS, MPP, Kevin Charette, CCP, Gregory Latham, MD, Christina L. Greene, MD, David Mauchley, MD, Andrew Koth, MD, D. Michael McMullan, MD
Publikováno v:
JTCVS Open, Vol 22, Iss , Pp 450-457 (2024)
Objective: To evaluate the effect of a blood conservation program on trends in use of donor blood products and early clinical outcomes in infants undergoing open heart surgery. Methods: Four hundred nine patients younger than age 1 year undergoing op
Externí odkaz:
https://doaj.org/article/c5e0664b51194eae9b0834e58d0a9440
Autor:
Pascal Bernier, Leandra Desjardins, Marie-Claude Charette, Jacinthe Harnois, Éloise Poirier, Karyne Daigle
Publikováno v:
Canadian Oncology Nursing Journal, Vol 34, Iss 4, Pp 505-513 (2024)
Introduction : Afin de mieux répondre aux besoins des jeunes atteints de cancer et de leur famille, un rôle novateur d’infirmier clinicien en santé mentale (ICSM) en hématologie-oncologie pédiatrique a vu le jour dans un centre hospitalier uni
Externí odkaz:
https://doaj.org/article/571d0057a6504169959bc2634440c973