2 citations · 2 across the 10 of their papers we have counts for
10 papers
Multi-Task Partially Supervised Learning for Super-Resolution and Semantic Segmentation on Earth Observation data
Hoàng-Ân Lê, Minh-Tan Pham, Solange Lemai-Chenevier +1
Super-resolution and semantic segmentation are known to benefit one another, especially in the Earth observation context. However, learning both tasks in a joint model often requir…
Leveraging Membership Inference Attacks for Privacy Measurement in Federated Learning for Remote Sensing Images
Anh-Kiet Duong, Petra Gomez-Krämer, Hoàng-Ân Lê +1
Federated Learning (FL) enables collaborative model training while keeping training data localized, allowing us to preserve privacy in various domains including remote sensing. How…
Enhancing deep learning performance on burned area delineation from SPOT-6/7 imagery for emergency management
Maria Rodriguez, Minh-Tan Pham, Martin Sudmanns +2
After a wildfire, delineating burned areas (BAs) is crucial for quantifying damages and supporting ecosystem recovery. Current BA mapping approaches rely on computer vision models…
Adaptive federated learning for ship detection across diverse satellite imagery sources
Tran-Vu La, Minh-Tan Pham, Yu Li +2
We investigate the application of Federated Learning (FL) for ship detection across diverse satellite datasets, offering a privacy-preserving solution that eliminates the need for…
Box for Mask and Mask for Box: weak losses for multi-task partially supervised learning
Hoàng-Ân Lê, Paul Berg, Minh-Tan Pham
Object detection and semantic segmentation are both scene understanding tasks yet they differ in data structure and information level. Object detection requires box coordinates for…
Leveraging knowledge distillation for partial multi-task learning from multiple remote sensing datasets
Hoàng-Ân Lê, Minh-Tan Pham
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 di…
Insight Into the Collocation of Multi-Source Satellite Imagery for Multi-Scale Vessel Detection
Tran-Vu La, Minh-Tan Pham, Marco Chini
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…
Leveraging feature communication in federated learning for remote sensing image classification
Anh-Kiet Duong, Hoàng-Ân Lê, Minh-Tan Pham
In the realm of Federated Learning (FL) applied to remote sensing image classification, this study introduces and assesses several innovative communication strategies. Our explorat…
Data exploitation: multi-task learning of object detection and semantic segmentation on partially annotated data
Hoàng-Ân Lê, Minh-Tan Pham
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 rel…
Self-Training and Multi-Task Learning for Limited Data: Evaluation Study on Object Detection
Hoàng-Ân Lê, Minh-Tan Pham
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…