Search Results for author:
Found 21 papers, 9 papers with code
Cross-sensor super-resolution of irregularly sampled Sentinel-2 time series
Satellite imaging generally presents a trade-off between the frequency of acquisitions and the spatial resolution of the images.
Detecting Out-Of-Distribution Earth Observation Images with Diffusion Models
In this work, we show that the reconstruction error of diffusion models can effectively serve as unsupervised out-of-distribution detectors for remote sensing images, using them as a plausibility score.
Semantic Generative Augmentations for Few-Shot Counting
This requires to generate images that correspond to a given input number of objects.
Ranked #3 on
Object Counting
on FSC147
(using extra training data)
Optimization of Rank Losses for Image Retrieval
It provides an upperbound for rank losses and ensures robust training.
Wasserstein Loss for Semantic Editing in the Latent Space of GANs
The latent space of GANs contains rich semantics reflecting the training data.
Hierarchical Average Precision Training for Pertinent Image Retrieval
Image Retrieval is commonly evaluated with Average Precision (AP) or Recall@k. Yet, those metrics, are limited to binary labels and do not take into account errors' severity.
Ranked #1 on
Metric Learning
on DyML-Animal
Efficient Autoprecoder-based deep learning for massive MU-MIMO Downlink under PA Non-Linearities
Specifically, we aim to design the PA-aware precoder and the receive decoder by leveraging the concept of autoprecoder, whereas the end-to-end massive multiuser (MU)-MIMO downlink is designed using a deep neural network (NN).
Multi-Attribute Balanced Sampling for Disentangled GAN Controls
We propose to address disentanglement by subsampling the generated data to remove over-represented co-occuring attributes thus balancing the semantics of the dataset before training the classifiers.
Robust and Decomposable Average Precision for Image Retrieval
In image retrieval, standard evaluation metrics rely on score ranking, e. g. average precision (AP).
Ranked #2 on
Image Retrieval
on CUB-200-2011
Web Image Context Extraction with Graph Neural Networks and Sentence Embeddings on the DOM tree
Web Image Context Extraction (WICE) consists in obtaining the textual information describing an image using the content of the surrounding webpage.
Semi-Supervised Semantic Segmentation in Earth Observation: The MiniFrance Suite, Dataset Analysis and Multi-task Network Study
The development of semi-supervised learning techniques is essential to enhance the generalization capacities of machine learning algorithms.
Distance transform regression for spatially-aware deep semantic segmentation
This work introduces a new semantic segmentation regularization based on the regression of a distance transform.
Multimodal deep networks for text and image-based document classification
Classification of document images is a critical step for archival of old manuscripts, online subscription and administrative procedures.
Deep Learning for Classification of Hyperspectral Data: A Comparative Review
1 This article is intended for both data scientists with interest in hyperspectral data and remote sensing experts eager to apply deep learning techniques to their own dataset.
Ranked #14 on
Hyperspectral Image Classification
on Pavia University
(Overall Accuracy metric)
Generative Adversarial Networks for Realistic Synthesis of Hyperspectral Samples
This work addresses the scarcity of annotated hyperspectral data required to train deep neural networks.
Beyond RGB: Very High Resolution Urban Remote Sensing With Multimodal Deep Networks
In this work, we investigate various methods to deal with semantic labeling of very high resolution multi-modal remote sensing data.
Joint Learning from Earth Observation and OpenStreetMap Data to Get Faster Better Semantic Maps
In this work, we investigate the use of OpenStreetMap data for semantic labeling of Earth Observation images.
Fusion of Heterogeneous Data in Convolutional Networks for Urban Semantic Labeling (Invited Paper)
We introduce residual correction as a way to learn how to fuse predictions coming out of a dual stream architecture.
On the usability of deep networks for object-based image analysis
In this work, we show how to use such deep networks to detect, segment and classify different varieties of wheeled vehicles in aerial images from the ISPRS Potsdam dataset.
How Useful is Region-based Classification of Remote Sensing Images in a Deep Learning Framework?
In this paper, we investigate the impact of segmentation algorithms as a preprocessing step for classification of remote sensing images in a deep learning framework.
Semantic Segmentation of Earth Observation Data Using Multimodal and Multi-scale Deep Networks
This work investigates the use of deep fully convolutional neural networks (DFCNN) for pixel-wise scene labeling of Earth Observation images.
