Zobrazeno 1 - 10
of 418
pro vyhledávání: '"Pham The Tan"'
Autor:
Uzun, Baki, Pande, Shivam, Cachin-Bernard, Gwendal, Pham, Minh-Tan, Lefèvre, Sébastien, Blatrix, Rumais, McKey, Doyle
Regular patterns of vegetation are considered widespread landscapes, although their global extent has never been estimated. Among them, spotted landscapes are of particular interest in the context of climate change. Indeed, regularly spaced vegetatio
Externí odkaz:
http://arxiv.org/abs/2409.00518
Autor:
Nguyen, Huy Hoang, Nguyen, Cuong Nhat, Dao, Xuan Tung, Duong, Quoc Trung, Kim, Dzung Pham Thi, Pham, Minh-Tan
This paper aims to conduct a comparative analysis of contemporary Variational Autoencoder (VAE) architectures employed in anomaly detection, elucidating their performance and behavioral characteristics within this specific task. The architectural con
Externí odkaz:
http://arxiv.org/abs/2408.13561
Autor:
Lê, Hoàng-Ân, Pham, Minh-Tan
Partial multi-task learning where training examples are annotated for one of the target tasks is a promising idea in remote sensing as it allows combining datasets annotated for different tasks and predicting more tasks with fewer network parameters.
Externí odkaz:
http://arxiv.org/abs/2405.15394
Ship detection from satellite imagery using Deep Learning (DL) is an indispensable solution for maritime surveillance. However, applying DL models trained on one dataset to others having differences in spatial resolution and radiometric features requ
Externí odkaz:
http://arxiv.org/abs/2403.13698
In the realm of Federated Learning (FL) applied to remote sensing image classification, this study introduces and assesses several innovative communication strategies. Our exploration includes feature-centric communication, pseudo-weight amalgamation
Externí odkaz:
http://arxiv.org/abs/2403.13575
Autor:
Lê, Hoàng-Ân, Pham, Minh-Tan
Multi-task partially annotated data where each data point is annotated for only a single task are potentially helpful for data scarcity if a network can leverage the inter-task relationship. In this paper, we study the joint learning of object detect
Externí odkaz:
http://arxiv.org/abs/2311.04040
Autor:
Lê, Hoàng-Ân, Pham, Minh-Tan
Self-training allows a network to learn from the predictions of a more complicated model, thus often requires well-trained teacher models and mixture of teacher-student data while multi-task learning jointly optimizes different targets to learn salie
Externí odkaz:
http://arxiv.org/abs/2309.06288
Wildfire detection using satellite images is a widely studied task in remote sensing with many applications to fire delineation and mapping. Recently, deep learning methods have become a scalable solution to automate this task, especially in the fiel
Externí odkaz:
http://arxiv.org/abs/2308.13367
Autor:
Lê, Hoàng-Ân, Pham, Minh-Tan
Knowledge distillation, a well-known model compression technique, is an active research area in both computer vision and remote sensing communities. In this paper, we evaluate in a remote sensing context various off-the-shelf object detection knowled
Externí odkaz:
http://arxiv.org/abs/2307.09264
Autor:
Belmouhcine, Abdelbadie, Burnel, Jean-Christophe, Courtrai, Luc, Pham, Minh-Tan, Lefèvre, Sébastien
Object detection in remote sensing is a crucial computer vision task that has seen significant advancements with deep learning techniques. However, most existing works in this area focus on the use of generic object detection and do not leverage the
Externí odkaz:
http://arxiv.org/abs/2307.06724