Zobrazeno 1 - 10
of 680
pro vyhledávání: '"Quang, Huy P"'
Data augmentation is a widely used technique for creating training data for tasks that require labeled data, such as semantic segmentation. This method benefits pixel-wise annotation tasks requiring much effort and intensive labor. Traditional data a
Externí odkaz:
http://arxiv.org/abs/2409.06002
This paper presents a fitted space-time finite element method for solving a parabolic advection-diffusion problem with a nonstationary interface. The jumping diffusion coefficient gives rise to the discontinuity of the spatial gradient of solution ac
Externí odkaz:
http://arxiv.org/abs/2407.08439
Numerical simulations are a highly valuable tool to evaluate the impact of the uncertainties of various modelparameters, and to optimize e.g. injection-production scenarios in the context of underground storage (of CO2typically). Finite volume approx
Externí odkaz:
http://arxiv.org/abs/2406.07950
In recent years, Large Language Models (LLMs) have become integrated into our daily lives, serving as invaluable assistants in completing tasks. Widely embraced by users, the abuse of LLMs is inevitable, particularly in using them to generate text co
Externí odkaz:
http://arxiv.org/abs/2405.03206
The quest for robust Person re-identification (Re-ID) systems capable of accurately identifying subjects across diverse scenarios remains a formidable challenge in surveillance and security applications. This study presents a novel methodology that s
Externí odkaz:
http://arxiv.org/abs/2405.01101
The emergence of deep learning techniques has advanced the image segmentation task, especially for medical images. Many neural network models have been introduced in the last decade bringing the automated segmentation accuracy close to manual segment
Externí odkaz:
http://arxiv.org/abs/2405.15779
Autor:
Bai, Yang, Ngoc, Phuoc Thanh Tran, Nguyen, Huu Duoc, Le, Duc Long, Ha, Quang Huy, Kai, Kazuki, To, Yu Xiang See, Deng, Yaosheng, Song, Jie, Wakamiya, Naoki, Sato, Hirotaka, Ogura, Masaki
Navigating multi-robot systems in complex terrains has always been a challenging task. This is due to the inherent limitations of traditional robots in collision avoidance, adaptation to unknown environments, and sustained energy efficiency. In order
Externí odkaz:
http://arxiv.org/abs/2403.17392
Semantic segmentation is crucial for autonomous driving, particularly for Drivable Area and Lane Segmentation, ensuring safety and navigation. To address the high computational costs of current state-of-the-art (SOTA) models, this paper introduces Tw
Externí odkaz:
http://arxiv.org/abs/2403.16958
Autor:
Nguyen, Quang-Huy, Zhou, Jin Peng, Liu, Zhenzhen, Bui, Khanh-Huyen, Weinberger, Kilian Q., Le, Dung D.
Machine learning algorithms are increasingly provided as black-box cloud services or pre-trained models, without access to their training data. This motivates the problem of zero-shot out-of-distribution (OOD) detection. Concretely, we aim to detect
Externí odkaz:
http://arxiv.org/abs/2402.03292
Pareto Set Learning (PSL) is a promising approach for approximating the entire Pareto front in multi-objective optimization (MOO) problems. However, existing derivative-free PSL methods are often unstable and inefficient, especially for expensive bla
Externí odkaz:
http://arxiv.org/abs/2311.15297