5 citations · 9 across the 8 of their papers we have counts for
8 papers
Variational Autoencoder for Anomaly Detection: A Comparative Study
Huy Hoang Nguyen, Cuong Nhat Nguyen, Xuan Tung Dao +3
This paper aims to conduct a comparative analysis of contemporary Variational Autoencoder (VAE) architectures employed in anomaly detection, elucidating their performance and behav…
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…
Burnt area extraction from high-resolution satellite images based on anomaly detection
Oscar David Rafael Narvaez Luces, Minh-Tan Pham, Quentin Poterek +1
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 be…
Knowledge Distillation for Object Detection: from generic to remote sensing datasets
Hoàng-Ân Lê, Minh-Tan Pham
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…
Multimodal Object Detection in Remote Sensing
Abdelbadie Belmouhcine, Jean-Christophe Burnel, Luc Courtrai +2
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…