2 citations · 2 across the 7 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…