Publication
Data for Paper "Scalable Semantic 3D Mapping of Coral Reefs with Deep Learning"
Publications associées (32)
Fast and Future: Towards Efficient Forecasting in Video Semantic Segmentation
Deep learning has revolutionized the field of computer vision, a success largely attributable to the growing size of models, datasets, and computational power.Simultaneously, a critical pain point arises as several computer vision applications are deployed ...
EPFL2024Land Cover Mapping From Multiple Complementary Experts Under Heavy Class Imbalance
Devis Tuia, Valérie Zermatten, Javiera Francisca Castillo Navarro, Xiaolong Lu
Deep learning has emerged as a promising avenue for automatic mapping, demonstrating high efficacy in land cover categorization through various semantic segmentation models. Nonetheless, the practical deployment of these models encounters important challen ...
Ieee-Inst Electrical Electronics Engineers Inc2024Advancing Self-Supervised Deep Learning for 3D Scene Understanding
Recent advancements in deep learning have revolutionized 3D computer vision, enabling the extraction of intricate 3D information from 2D images and video sequences. This thesis explores the application of deep learning in three crucial challenges of 3D com ...
EPFL2024Multi-temporal forest monitoring in the Swiss Alps with knowledge-guided deep learning
Devis Tuia, Gaston Jean Lenczner, Thiên-Anh Claris Nguyen, Marc Conrad Russwurm
Monitoring forests, in particular their response to climate and land use change, requires studying long time scales. While efficient deep learning methods have been developed to process short time series of satellite imagery, leveraging long time series of ...
Elsevier Science Inc2024Coronal jets identification using Deep Learning as Image and Video Object Detection
This report presents a study on the development and application of a Region-based Convolutional Neural Network, Faster RCNN and a more complex one, TransVOD, to locate solar coronal jets using data from the Solar Dynamic Observatory (SDO). The study focus ...
2024SAGTTA: SALIENCY GUIDED TEST TIME AUGMENTATION FOR MEDICAL IMAGE SEGMENTATION ACROSS VENDOR DOMAIN SHIFT
Test time augmentation has been shown to be an effective approach to combat domain shifts in deep learning. Despite their promising performance levels, the interpretability of the underlying used models is however low. Saliency maps have been widely used i ...
New York2023Confidence Matters: Applications to Semantic Segmentation
The successes of deep learning for semantic segmentation can in be, in part, attributed to its scale: a notion that encapsulates the largeness of these computational architectures and the labeled datasets they are trained on. These resource requirements hi ...
EPFL2023Why is the winner the best?
Jian Wang, Gabriel Girard, Ho Ling Li, Adrien Raphaël Depeursinge, Yong Yang, Fan Xia, Xiao Wang, Jing Li, Hui Wang
International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really gener ...
Los Alamitos2023GALET: A deep learning image segmentation model for drone-based grain size analysis of gravel bars
Giovanni De Cesare, Christian Mörtl
In this study, we present the deep learning image segmentation model for drone-based grain size analysis of gravel bars called GALET. The objectives are to quantify the performance of the code and to test its applicability in river research and management. ...
International Association for Hydro-Environment Engineering and Research (IAHR)2022Deep learning-based analysis of multiple sclerosis lesions with high and ultra-high field MRI
Multiple sclerosis (MS) is the most common demyelinating disease of the central nervous system and affects almost 3 million people worldwide. There is currently no cure for MS, and its symptoms, starting with fatigue and weakness, often progress over time ...
EPFL2022