research
∙
01/19/2023
Fast Inference in Denoising Diffusion Models via MMD Finetuning
Denoising Diffusion Models (DDMs) have become a popular tool for generat...
share
research
∙
09/21/2022
Rethinking the compositionality of point clouds through regularization in the hyperbolic space
Point clouds of 3D objects exhibit an inherent compositional nature wher...
research
∙
09/20/2022
Cross-modal Learning for Image-Guided Point Cloud Shape Completion
In this paper we explore the recent topic of point cloud completion, gui...
research
∙
07/01/2022
Exploring the solution space of linear inverse problems with GAN latent geometry
Inverse problems consist in reconstructing signals from incomplete sets ...
research
∙
04/06/2022
Super-resolved multi-temporal segmentation with deep permutation-invariant networks
Multi-image super-resolution from multi-temporal satellite acquisitions ...
research
∙
08/20/2021
Self-supervised learning for joint SAR and multispectral land cover classification
Self-supervised learning techniques are gaining popularity due to their ...
research
∙
05/26/2021
Permutation invariance and uncertainty in multitemporal image super-resolution
Recent advances have shown how deep neural networks can be extremely eff...
research
∙
03/30/2021
Denoise and Contrast for Category Agnostic Shape Completion
In this paper, we present a deep learning model that exploits the power ...
research
∙
03/29/2021
RAN-GNNs: breaking the capacity limits of graph neural networks
Graph neural networks have become a staple in problems addressing learni...
research
∙
12/10/2020
Deep learning methods for SAR image despeckling: trends and perspectives
Synthetic aperture radar (SAR) images are affected by a spatially-correl...
research
∙
10/29/2020
Beyond cross-entropy: learning highly separable feature distributions for robust and accurate classification
Deep learning has shown outstanding performance in several applications ...
research
∙
08/13/2020
BioMetricNet: deep unconstrained face verification through learning of metrics regularized onto Gaussian distributions
We present BioMetricNet: a novel framework for deep unconstrained face v...
research
∙
07/06/2020
Learning Graph-Convolutional Representations for Point Cloud Denoising
Point clouds are an increasingly relevant data type but they are often c...
research
∙
07/04/2020
Speckle2Void: Deep Self-Supervised SAR Despeckling with Blind-Spot Convolutional Neural Networks
Information extraction from synthetic aperture radar (SAR) images is hea...
research
∙
01/15/2020
Towards Deep Unsupervised SAR Despeckling with Blind-Spot Convolutional Neural Networks
SAR despeckling is a problem of paramount importance in remote sensing, ...
research
∙
01/15/2020
DeepSUM++: Non-local Deep Neural Network for Super-Resolution of Unregistered Multitemporal Images
Deep learning methods for super-resolution of a remote sensing scene fro...
research
∙
11/20/2019
Learning mappings onto regularized latent spaces for biometric authentication
We propose a novel architecture for generic biometric authentication bas...
research
∙
09/02/2019
Analysis of SparseHash: an efficient embedding of set-similarity via sparse projections
Embeddings provide compact representations of signals in order to perfor...
research
∙
07/19/2019
Deep Graph-Convolutional Image Denoising
Non-local self-similarity is well-known to be an effective prior for the...
research
∙
07/15/2019
DeepSUM: Deep neural network for Super-resolution of Unregistered Multitemporal images
Recently, convolutional neural networks (CNN) have been successfully app...
research
∙
07/05/2019
High-throughput Onboard Hyperspectral Image Compression with Ground-based CNN Reconstruction
Compression of hyperspectral images onboard of spacecrafts is a tradeoff...
research
∙
05/29/2019
Image Denoising with Graph-Convolutional Neural Networks
Recovering an image from a noisy observation is a key problem in signal ...
research
∙
04/17/2018
Sampling of graph signals via randomized local aggregations
Sampling of signals defined over the nodes of a graph is one of the cruc...
research
∙
07/07/2017
GPU-Accelerated Algorithms for Compressed Signals Recovery with Application to Astronomical Imagery Deblurring
Compressive sensing promises to enable bandwidth-efficient on-board comp...
research
∙
10/12/2016
Fast Training of Convolutional Neural Networks via Kernel Rescaling
Training deep Convolutional Neural Networks (CNN) is a time consuming ta...
research
∙
11/04/2013
A Parallel Compressive Imaging Architecture for One-Shot Acquisition
A limitation of many compressive imaging architectures lies in the seque...
research
∙
10/08/2013
Smoothness-Constrained Image Recovery from Block-Based Random Projections
In this paper we address the problem of visual quality of images reconst...
research
∙
10/04/2013
A Novel Progressive Image Scanning and Reconstruction Scheme based on Compressed Sensing and Linear Prediction
Compressed sensing (CS) is an innovative technique allowing to represent...
research
∙
10/04/2013
