Zobrazeno 11 - 20
of 2 942
pro vyhledávání: '"Li, Jonathan"'
Autor:
Osco, Lucas Prado, Wu, Qiusheng, de Lemos, Eduardo Lopes, Gonçalves, Wesley Nunes, Ramos, Ana Paula Marques, Li, Jonathan, Junior, José Marcato
Segmentation is an essential step for remote sensing image processing. This study aims to advance the application of the Segment Anything Model (SAM), an innovative image segmentation model by Meta AI, in the field of remote sensing image analysis. S
Externí odkaz:
http://arxiv.org/abs/2306.16623
In the domain of remote sensing image interpretation, road extraction from high-resolution aerial imagery has already been a hot research topic. Although deep CNNs have presented excellent results for semantic segmentation, the efficiency and capabil
Externí odkaz:
http://arxiv.org/abs/2306.04947
Point cloud segmentation is one of the most important tasks in computer vision with widespread scientific, industrial, and commercial applications. The research thereof has resulted in many breakthroughs in 3D object and scene understanding. Previous
Externí odkaz:
http://arxiv.org/abs/2306.08073
Parameter-efficient tuning aims to mitigate the large memory requirements of adapting pretrained language models for downstream tasks. For example, one popular method, prefix-tuning, prepends trainable tokens to sequences while freezing the rest of t
Externí odkaz:
http://arxiv.org/abs/2305.12086
Autor:
Goncalves, Diogo Nunes, Junior, Jose Marcato, Zamboni, Pedro, Pistori, Hemerson, Li, Jonathan, Nogueira, Keiller, Goncalves, Wesley Nunes
Multi-task learning has proven to be effective in improving the performance of correlated tasks. Most of the existing methods use a backbone to extract initial features with independent branches for each task, and the exchange of information between
Externí odkaz:
http://arxiv.org/abs/2305.02813
Autor:
Li, Jonathan Yu-Meng
We introduce a robust variant of the Kelly portfolio optimization model, called the Wasserstein-Kelly portfolio optimization. Our model, taking a Wasserstein distributionally robust optimization (DRO) formulation, addresses the fundamental issue of e
Externí odkaz:
http://arxiv.org/abs/2302.13979
Wasserstein distributionally robust optimization (DRO) has found success in operations research and machine learning applications as a powerful means to obtain solutions with favourable out-of-sample performances. Two compelling explanations for the
Externí odkaz:
http://arxiv.org/abs/2212.05716
Autor:
Li, Jonathan
ALS is a devastating neurodegenerative disorder with no known cure. In this thesis, I describe high-throughput omics characterization of patient-derived induced pluripotent stem cell (iPSC) models. I present analyses aimed at discovering disease path
Seeking legal advice is often expensive. Recent advancements in machine learning for solving complex problems can be leveraged to help make legal services more accessible to the public. However, real-life applications encounter significant challenges
Externí odkaz:
http://arxiv.org/abs/2210.13712
Neural Radiance Field (NeRF) has recently become a significant development in the field of Computer Vision, allowing for implicit, neural network-based scene representation and novel view synthesis. NeRF models have found diverse applications in robo
Externí odkaz:
http://arxiv.org/abs/2210.00379